Programming Assignment - 3: Recurrent Neural Networks¶
Loading the data¶
In [15]:
# import packages
import random
import numpy as np
import seaborn as sns
import cv2 as cv
import matplotlib.pyplot as plt
import sklearn
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim
from torch.utils.data import DataLoader, Subset
import torchvision
import torchvision.transforms as transforms
from torchsummary import summary
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay
%matplotlib inline
plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
1. MNIST classification using RNN¶
In [2]:
mnist_train = torchvision.datasets.MNIST(root = 'MNIST', download = True, transform = transforms.ToTensor())
mnist_data_test = torchvision.datasets.MNIST(root = 'MNIST', train = False, download = True, transform = transforms.ToTensor())
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to MNIST/MNIST/raw/train-images-idx3-ubyte.gz
Extracting MNIST/MNIST/raw/train-images-idx3-ubyte.gz to MNIST/MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to MNIST/MNIST/raw/train-labels-idx1-ubyte.gz
Extracting MNIST/MNIST/raw/train-labels-idx1-ubyte.gz to MNIST/MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz
Extracting MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz to MNIST/MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to MNIST/MNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting MNIST/MNIST/raw/t10k-labels-idx1-ubyte.gz to MNIST/MNIST/raw
In [3]:
train_indices, val_indices, _, _ = train_test_split(
range(len(mnist_train)),
mnist_train.targets,
stratify = mnist_train.targets,
test_size = 0.2,
random_state = 42
)
# generate subset based on indices
mnist_data_train = Subset(mnist_train, train_indices)
mnist_data_val = Subset(mnist_train, val_indices)
In [4]:
# Parameters
BATCH_SIZE = 64
CLASSES = 10
In [5]:
train_loader = DataLoader(dataset = mnist_data_train, batch_size = BATCH_SIZE, shuffle = True)
val_loader = DataLoader(dataset = mnist_data_val, batch_size = len(mnist_data_val), shuffle = False)
test_loader = DataLoader(dataset = mnist_data_test, batch_size = len(mnist_data_test), shuffle = False)
RNN¶
In [ ]:
class RNN(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, layers = 1):
super().__init__()
self.rnn = nn.RNN(input_dim, hidden_dim, num_layers = layers, batch_first = True)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, text):
# text = text.reshape(28, BATCH_SIZE, 28)
#text = [sent len, batch size]
#embedded = [sent len, batch size, emb dim]
output, hidden = self.rnn(text)
#output = [sent len, batch size, hid dim]
#hidden = [1, batch size, hid dim]
# assert torch.equal(output[-1,:,:], hidden.squeeze(0))
# hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)
return self.fc(output[:, -1, :])
In [ ]:
INPUT_DIM = 28
HIDDEN_DIM = 150
OUTPUT_DIM = 10
NUM_LAYERS = 5
LR = 0.001
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [ ]:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [ ]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for epoch in range(5): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# inputs_reshape = inputs.reshape(28, BATCH_SIZE, 28)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs.view(-1, 28, 28))
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if i % 200 == 0:
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
with torch.no_grad():
for j, data in enumerate(train_loader, 0):
images, labels = data
# Forward pass only to get logits/output
outputs = model(images.view(-1, 28, 28))
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
for j, data in enumerate(val_loader, 0):
images, labels = data
# Forward pass only to get logits/output
outputs = model(images.view(-1, 28, 28))
val_cost = criterion(outputs, labels)
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Epoch: {}. Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(epoch, i, loss.item(), accuracy))
print('Finished Training')
Epoch: 0. Iteration: 0. Train Loss: 2.3033831119537354. Validation Accuracy: 17.683332443237305 Epoch: 0. Iteration: 200. Train Loss: 0.7841010093688965. Validation Accuracy: 78.68333435058594 Epoch: 0. Iteration: 400. Train Loss: 0.6435156464576721. Validation Accuracy: 83.18333435058594 Epoch: 0. Iteration: 600. Train Loss: 0.5123307704925537. Validation Accuracy: 87.80000305175781 Epoch: 1. Iteration: 0. Train Loss: 0.3260912001132965. Validation Accuracy: 89.68333435058594 Epoch: 1. Iteration: 200. Train Loss: 0.2625710070133209. Validation Accuracy: 90.3499984741211 Epoch: 1. Iteration: 400. Train Loss: 0.25889888405799866. Validation Accuracy: 92.16666412353516 Epoch: 1. Iteration: 600. Train Loss: 0.21165187656879425. Validation Accuracy: 93.0999984741211 Epoch: 2. Iteration: 0. Train Loss: 0.38629692792892456. Validation Accuracy: 90.75 Epoch: 2. Iteration: 200. Train Loss: 0.31491878628730774. Validation Accuracy: 90.04166412353516 Epoch: 2. Iteration: 400. Train Loss: 0.18339726328849792. Validation Accuracy: 92.9749984741211 Epoch: 2. Iteration: 600. Train Loss: 0.21403656899929047. Validation Accuracy: 92.83333587646484 Epoch: 3. Iteration: 0. Train Loss: 0.18808984756469727. Validation Accuracy: 93.1500015258789 Epoch: 3. Iteration: 200. Train Loss: 0.35917243361473083. Validation Accuracy: 92.35832977294922 Epoch: 3. Iteration: 400. Train Loss: 0.16868257522583008. Validation Accuracy: 93.90833282470703 Epoch: 3. Iteration: 600. Train Loss: 0.22843490540981293. Validation Accuracy: 94.80000305175781 Epoch: 4. Iteration: 0. Train Loss: 0.28337737917900085. Validation Accuracy: 94.5999984741211 Epoch: 4. Iteration: 200. Train Loss: 0.16759584844112396. Validation Accuracy: 94.25833129882812 Epoch: 4. Iteration: 400. Train Loss: 0.13837425410747528. Validation Accuracy: 95.65833282470703 Epoch: 4. Iteration: 600. Train Loss: 0.02303224988281727. Validation Accuracy: 94.93333435058594 Finished Training
Learning curves¶
In [ ]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [ ]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Performance on the Test set¶
In [ ]:
for images, labels in test_loader:
# Forward pass only to get logits/output
outputs = model(images.view(-1, 28, 28))
test_cost = criterion(outputs, labels)
print("Cost function value: " + str(test_cost.item()))
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
print("Accuracy: " + str(accuracy_score(labels.numpy(), predicted.numpy())))
print("Classification Report: \n")
print(classification_report(labels.numpy(), predicted.numpy(), zero_division = 0))
print("Confusion Matrix: \n")
cm = confusion_matrix(labels.numpy(), predicted.numpy(), labels = range(10))
disp = ConfusionMatrixDisplay(confusion_matrix = cm,
display_labels = range(10))
disp.plot()
plt.show()
Cost function value: 0.16690757870674133
Accuracy: 0.9477
Classification Report:
precision recall f1-score support
0 0.98 0.98 0.98 980
1 0.98 0.98 0.98 1135
2 0.97 0.94 0.96 1032
3 0.97 0.93 0.95 1010
4 0.90 0.94 0.92 982
5 0.91 0.92 0.91 892
6 0.94 0.97 0.95 958
7 0.98 0.95 0.96 1028
8 0.92 0.97 0.95 974
9 0.92 0.90 0.91 1009
accuracy 0.95 10000
macro avg 0.95 0.95 0.95 10000
weighted avg 0.95 0.95 0.95 10000
Confusion Matrix:
Performance on randomly selected images¶
In [ ]:
images, labels = iter(test_loader).next()
In [ ]:
fig, ax = plt.subplots(1, 4, figsize = (20, 20))
for i in range(4):
idx = random.randint(0, len(mnist_data_test) - 1)
image = images[idx:idx + 1]
label = labels[idx]
_, predicted = torch.max(model(image.view(-1, 28, 28)).data, 1)
ax[i].imshow(image[0, 0, :, :])
ax[i].set_title('Predicted: {} Actual: {}'.format(predicted.item(), label.item()))
LSTM¶
In [6]:
class RNN(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, layers = 1):
super().__init__()
self.rnn = nn.LSTM(input_dim, hidden_dim, num_layers = layers, batch_first = True)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, text):
# text = text.reshape(28, BATCH_SIZE, 28)
#text = [sent len, batch size]
#embedded = [sent len, batch size, emb dim]
output, (hidden, cell) = self.rnn(text)
#output = [sent len, batch size, hid dim]
#hidden = [1, batch size, hid dim]
# assert torch.equal(output[-1,:,:], hidden.squeeze(0))
# hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)
return self.fc(output[:, -1, :])
In [7]:
INPUT_DIM = 28
HIDDEN_DIM = 150
OUTPUT_DIM = 10
NUM_LAYERS = 5
LR = 0.001
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [8]:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [9]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# inputs_reshape = inputs.reshape(28, BATCH_SIZE, 28)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs.view(-1, 28, 28))
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if i % 200 == 0:
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
with torch.no_grad():
for j, data in enumerate(train_loader, 0):
images, labels = data
# Forward pass only to get logits/output
outputs = model(images.view(-1, 28, 28))
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
for j, data in enumerate(val_loader, 0):
images, labels = data
# Forward pass only to get logits/output
outputs = model(images.view(-1, 28, 28))
val_cost = criterion(outputs, labels)
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Epoch: {}. Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(epoch, i, loss.item(), accuracy))
print('Finished Training')
Epoch: 0. Iteration: 0. Train Loss: 2.3079264163970947. Validation Accuracy: 9.933333396911621 Epoch: 0. Iteration: 200. Train Loss: 0.6569309830665588. Validation Accuracy: 69.78333282470703 Epoch: 0. Iteration: 400. Train Loss: 0.37728381156921387. Validation Accuracy: 90.46666717529297 Epoch: 0. Iteration: 600. Train Loss: 0.08544596284627914. Validation Accuracy: 93.5999984741211 Epoch: 1. Iteration: 0. Train Loss: 0.26060977578163147. Validation Accuracy: 94.0 Epoch: 1. Iteration: 200. Train Loss: 0.13048739731311798. Validation Accuracy: 95.4749984741211 Epoch: 1. Iteration: 400. Train Loss: 0.16586972773075104. Validation Accuracy: 95.69999694824219 Epoch: 1. Iteration: 600. Train Loss: 0.19795846939086914. Validation Accuracy: 96.25833129882812 Finished Training
Learning curves¶
In [10]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [11]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Performance on the Test set¶
In [12]:
for images, labels in test_loader:
# Forward pass only to get logits/output
outputs = model(images.view(-1, 28, 28))
test_cost = criterion(outputs, labels)
print("Cost function value: " + str(test_cost.item()))
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
print("Accuracy: " + str(accuracy_score(labels.numpy(), predicted.numpy())))
print("Classification Report: \n")
print(classification_report(labels.numpy(), predicted.numpy(), zero_division = 0))
print("Confusion Matrix: \n")
cm = confusion_matrix(labels.numpy(), predicted.numpy(), labels = range(10))
disp = ConfusionMatrixDisplay(confusion_matrix = cm,
display_labels = range(10))
disp.plot()
plt.show()
Cost function value: 0.13196255266666412
Accuracy: 0.9633
Classification Report:
precision recall f1-score support
0 0.99 0.98 0.99 980
1 0.99 0.98 0.98 1135
2 0.96 0.98 0.97 1032
3 0.97 0.96 0.97 1010
4 0.98 0.96 0.97 982
5 0.96 0.96 0.96 892
6 0.97 0.99 0.98 958
7 0.91 0.98 0.94 1028
8 0.97 0.94 0.95 974
9 0.95 0.90 0.93 1009
accuracy 0.96 10000
macro avg 0.96 0.96 0.96 10000
weighted avg 0.96 0.96 0.96 10000
Confusion Matrix:
Performance on randomly selected images¶
In [13]:
images, labels = iter(test_loader).next()
In [14]:
fig, ax = plt.subplots(1, 4, figsize = (20, 20))
for i in range(4):
idx = random.randint(0, len(mnist_data_test) - 1)
image = images[idx:idx + 1]
label = labels[idx]
_, predicted = torch.max(model(image.view(-1, 28, 28)).data, 1)
ax[i].imshow(image[0, 0, :, :])
ax[i].set_title('Predicted: {} Actual: {}'.format(predicted.item(), label.item()))
Performance on custom handwritten images¶
In [ ]:
X = []
y = []
for i in range(10):
a = cv.imread(str(i) + '_resize.jpg', cv.IMREAD_GRAYSCALE)
X.append(a)
y.append(i)
X = torch.tensor(np.array(X)).float()
y = torch.tensor(np.array(y)).long()
In [ ]:
outputs = model(X)
_, predicted = torch.max(outputs.data, 1)
In [ ]:
fig, ax = plt.subplots(2, 5, figsize = (20, 20))
for i in range(10):
r = i // 5
c = i % 5
ax[r, c].imshow(X[i, :, :].detach().numpy())
ax[r, c].set_title('Predicted: {}'.format(predicted[i].item()))
With LSTM being the best model so far, we test it on custom handwritten digits. We can see that the model makes poor predictions. Thus, our model is not able to generalize to inputs of different format (MNIST images have a dark background compared to the custom images)
RNN - Bidirectional¶
In [ ]:
class RNN(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, layers = 1):
super().__init__()
self.rnn = nn.RNN(input_dim, hidden_dim, num_layers = layers, batch_first = True, bidirectional = True)
self.fc = nn.Linear(2 * hidden_dim, output_dim)
def forward(self, text):
# text = text.reshape(28, BATCH_SIZE, 28)
#text = [sent len, batch size]
#embedded = [sent len, batch size, emb dim]
output, hidden = self.rnn(text)
#output = [sent len, batch size, hid dim]
#hidden = [1, batch size, hid dim]
# assert torch.equal(output[-1,:,:], hidden.squeeze(0))
# hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)
return self.fc(output[:, -1, :])
In [ ]:
INPUT_DIM = 28
HIDDEN_DIM = 150
OUTPUT_DIM = 10
NUM_LAYERS = 5
LR = 0.001
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [ ]:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [ ]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for epoch in range(4): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# inputs_reshape = inputs.reshape(28, BATCH_SIZE, 28)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs.view(-1, 28, 28))
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if i % 200 == 0:
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
with torch.no_grad():
for j, data in enumerate(train_loader, 0):
images, labels = data
# Forward pass only to get logits/output
outputs = model(images.view(-1, 28, 28))
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
for j, data in enumerate(val_loader, 0):
images, labels = data
# Forward pass only to get logits/output
outputs = model(images.view(-1, 28, 28))
val_cost = criterion(outputs, labels)
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Epoch: {}. Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(epoch, i, loss.item(), accuracy))
print('Finished Training')
Epoch: 0. Iteration: 0. Train Loss: 2.3071086406707764. Validation Accuracy: 13.274999618530273 Epoch: 0. Iteration: 200. Train Loss: 0.5577687621116638. Validation Accuracy: 77.38333129882812 Epoch: 0. Iteration: 400. Train Loss: 0.7612340450286865. Validation Accuracy: 82.7249984741211 Epoch: 0. Iteration: 600. Train Loss: 0.2962421476840973. Validation Accuracy: 84.18333435058594 Epoch: 1. Iteration: 0. Train Loss: 0.23228172957897186. Validation Accuracy: 89.82499694824219 Epoch: 1. Iteration: 200. Train Loss: 0.212496817111969. Validation Accuracy: 92.10832977294922 Epoch: 1. Iteration: 400. Train Loss: 0.5763186812400818. Validation Accuracy: 81.73332977294922 Epoch: 1. Iteration: 600. Train Loss: 0.13227100670337677. Validation Accuracy: 92.375 Epoch: 2. Iteration: 0. Train Loss: 0.06729239225387573. Validation Accuracy: 92.42500305175781 Epoch: 2. Iteration: 200. Train Loss: 0.19752027094364166. Validation Accuracy: 92.54166412353516 Epoch: 2. Iteration: 400. Train Loss: 0.24497781693935394. Validation Accuracy: 93.94999694824219 Epoch: 2. Iteration: 600. Train Loss: 0.0707588717341423. Validation Accuracy: 94.07499694824219 Epoch: 3. Iteration: 0. Train Loss: 0.25272515416145325. Validation Accuracy: 93.21666717529297 Epoch: 3. Iteration: 200. Train Loss: 0.20829549431800842. Validation Accuracy: 94.56666564941406 Epoch: 3. Iteration: 400. Train Loss: 0.28613561391830444. Validation Accuracy: 93.69166564941406 Epoch: 3. Iteration: 600. Train Loss: 0.18277205526828766. Validation Accuracy: 94.0 Finished Training
Learning curves¶
In [ ]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [ ]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Performance on the Test set¶
In [ ]:
for images, labels in test_loader:
# Forward pass only to get logits/output
outputs = model(images.view(-1, 28, 28))
test_cost = criterion(outputs, labels)
print("Cost function value: " + str(test_cost.item()))
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
print("Accuracy: " + str(accuracy_score(labels.numpy(), predicted.numpy())))
print("Classification Report: \n")
print(classification_report(labels.numpy(), predicted.numpy(), zero_division = 0))
print("Confusion Matrix: \n")
cm = confusion_matrix(labels.numpy(), predicted.numpy(), labels = range(10))
disp = ConfusionMatrixDisplay(confusion_matrix = cm,
display_labels = range(10))
disp.plot()
plt.show()
Cost function value: 0.2095593959093094
Accuracy: 0.9396
Classification Report:
precision recall f1-score support
0 0.98 0.94 0.96 980
1 0.97 0.98 0.98 1135
2 0.94 0.97 0.95 1032
3 0.96 0.93 0.94 1010
4 0.96 0.89 0.92 982
5 0.88 0.93 0.91 892
6 0.91 0.98 0.94 958
7 0.92 0.97 0.94 1028
8 0.95 0.92 0.94 974
9 0.92 0.87 0.90 1009
accuracy 0.94 10000
macro avg 0.94 0.94 0.94 10000
weighted avg 0.94 0.94 0.94 10000
Confusion Matrix:
Performance on randomly selected images¶
In [ ]:
images, labels = iter(test_loader).next()
In [ ]:
fig, ax = plt.subplots(1, 4, figsize = (20, 20))
for i in range(4):
idx = random.randint(0, len(mnist_data_test) - 1)
image = images[idx:idx + 1]
label = labels[idx]
_, predicted = torch.max(model(image.view(-1, 28, 28)).data, 1)
ax[i].imshow(image[0, 0, :, :])
ax[i].set_title('Predicted: {} Actual: {}'.format(predicted.item(), label.item()))
2. Remembering the number at a particular index in a given sequence¶
In [ ]:
def get_batch(K, L, pos = 1):
seq = []
X = np.zeros((K, L, 10))
y = np.zeros(K)
for i in range(K):
a = random.sample(range(10), L)
seq.append(a)
X[i, range(L), a] = 1
y[i] = a[pos]
return seq, torch.tensor(X).float(), torch.tensor(y).long()
In [ ]:
class RNN(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, layers = 1):
super().__init__()
self.layers = layers
self.hidden_dim = hidden_dim
self.rnn = nn.RNN(input_dim, hidden_dim, num_layers = layers, batch_first = True)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, text):
#text = [sent len, batch size]
#embedded = [sent len, batch size, emb dim]
output, hidden = self.rnn(text)
#output = [sent len, batch size, hid dim]
#hidden = [1, batch size, hid dim]
# assert torch.equal(output[-1,:,:], hidden.squeeze(0))
# hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)
return self.fc(output[:, -1, :])
Hidden state size = 2¶
In [ ]:
INPUT_DIM = 10
HIDDEN_DIM = 2
OUTPUT_DIM = 10
NUM_LAYERS = 5
LR = 0.001
K = 64
pos = 1
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [ ]:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [ ]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for iteration in range(10*1000):
L = random.randint(3, 10)
_, inputs, labels = get_batch(K, L, pos = pos)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
_, inputs, labels = get_batch(K, L, pos = pos)
# Forward pass only to get logits/output
outputs = model(inputs)
val_cost = criterion(outputs, labels)
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(iteration, loss.item(), accuracy))
print('Finished Training')
Streaming output truncated to the last 5000 lines.
Iteration: 5001. Train Loss: 1.7336585521697998. Validation Accuracy: 23.4375
Iteration: 5002. Train Loss: 1.6839234828948975. Validation Accuracy: 29.6875
Iteration: 5003. Train Loss: 1.7300430536270142. Validation Accuracy: 31.25
Iteration: 5004. Train Loss: 1.852378487586975. Validation Accuracy: 18.75
Iteration: 5005. Train Loss: 1.6831579208374023. Validation Accuracy: 34.375
Iteration: 5006. Train Loss: 1.5855560302734375. Validation Accuracy: 29.6875
Iteration: 5007. Train Loss: 1.6942706108093262. Validation Accuracy: 34.375
Iteration: 5008. Train Loss: 1.6877472400665283. Validation Accuracy: 17.1875
Iteration: 5009. Train Loss: 1.7153151035308838. Validation Accuracy: 31.25
Iteration: 5010. Train Loss: 1.7098232507705688. Validation Accuracy: 35.9375
Iteration: 5011. Train Loss: 1.6397866010665894. Validation Accuracy: 35.9375
Iteration: 5012. Train Loss: 1.5586049556732178. Validation Accuracy: 39.0625
Iteration: 5013. Train Loss: 1.665091633796692. Validation Accuracy: 31.25
Iteration: 5014. Train Loss: 1.6989917755126953. Validation Accuracy: 28.125
Iteration: 5015. Train Loss: 1.622366189956665. Validation Accuracy: 23.4375
Iteration: 5016. Train Loss: 1.5868176221847534. Validation Accuracy: 29.6875
Iteration: 5017. Train Loss: 1.5863420963287354. Validation Accuracy: 40.625
Iteration: 5018. Train Loss: 1.7200210094451904. Validation Accuracy: 34.375
Iteration: 5019. Train Loss: 1.6288561820983887. Validation Accuracy: 32.8125
Iteration: 5020. Train Loss: 1.717681646347046. Validation Accuracy: 32.8125
Iteration: 5021. Train Loss: 1.7198069095611572. Validation Accuracy: 18.75
Iteration: 5022. Train Loss: 1.7704925537109375. Validation Accuracy: 29.6875
Iteration: 5023. Train Loss: 1.770250678062439. Validation Accuracy: 23.4375
Iteration: 5024. Train Loss: 1.8721400499343872. Validation Accuracy: 21.875
Iteration: 5025. Train Loss: 1.7334339618682861. Validation Accuracy: 21.875
Iteration: 5026. Train Loss: 1.8270658254623413. Validation Accuracy: 28.125
Iteration: 5027. Train Loss: 1.7914543151855469. Validation Accuracy: 31.25
Iteration: 5028. Train Loss: 1.7281785011291504. Validation Accuracy: 18.75
Iteration: 5029. Train Loss: 1.7110283374786377. Validation Accuracy: 25.0
Iteration: 5030. Train Loss: 1.7067950963974. Validation Accuracy: 26.5625
Iteration: 5031. Train Loss: 1.7436379194259644. Validation Accuracy: 26.5625
Iteration: 5032. Train Loss: 1.602404236793518. Validation Accuracy: 42.1875
Iteration: 5033. Train Loss: 1.7679624557495117. Validation Accuracy: 23.4375
Iteration: 5034. Train Loss: 1.5776642560958862. Validation Accuracy: 26.5625
Iteration: 5035. Train Loss: 1.7030174732208252. Validation Accuracy: 45.3125
Iteration: 5036. Train Loss: 1.7006993293762207. Validation Accuracy: 21.875
Iteration: 5037. Train Loss: 1.62148118019104. Validation Accuracy: 29.6875
Iteration: 5038. Train Loss: 1.9177346229553223. Validation Accuracy: 28.125
Iteration: 5039. Train Loss: 1.666070580482483. Validation Accuracy: 12.5
Iteration: 5040. Train Loss: 1.7114713191986084. Validation Accuracy: 28.125
Iteration: 5041. Train Loss: 1.7467368841171265. Validation Accuracy: 29.6875
Iteration: 5042. Train Loss: 1.6732933521270752. Validation Accuracy: 31.25
Iteration: 5043. Train Loss: 1.7064579725265503. Validation Accuracy: 25.0
Iteration: 5044. Train Loss: 1.7209426164627075. Validation Accuracy: 34.375
Iteration: 5045. Train Loss: 1.650127649307251. Validation Accuracy: 26.5625
Iteration: 5046. Train Loss: 1.646365761756897. Validation Accuracy: 21.875
Iteration: 5047. Train Loss: 1.660813331604004. Validation Accuracy: 25.0
Iteration: 5048. Train Loss: 1.686933994293213. Validation Accuracy: 34.375
Iteration: 5049. Train Loss: 1.8418511152267456. Validation Accuracy: 25.0
Iteration: 5050. Train Loss: 1.6164900064468384. Validation Accuracy: 26.5625
Iteration: 5051. Train Loss: 1.577942132949829. Validation Accuracy: 26.5625
Iteration: 5052. Train Loss: 1.809624195098877. Validation Accuracy: 31.25
Iteration: 5053. Train Loss: 1.7084203958511353. Validation Accuracy: 17.1875
Iteration: 5054. Train Loss: 1.9925967454910278. Validation Accuracy: 25.0
Iteration: 5055. Train Loss: 1.7457659244537354. Validation Accuracy: 32.8125
Iteration: 5056. Train Loss: 1.5339847803115845. Validation Accuracy: 31.25
Iteration: 5057. Train Loss: 1.7118803262710571. Validation Accuracy: 26.5625
Iteration: 5058. Train Loss: 1.6789358854293823. Validation Accuracy: 28.125
Iteration: 5059. Train Loss: 1.6877892017364502. Validation Accuracy: 20.3125
Iteration: 5060. Train Loss: 1.6270532608032227. Validation Accuracy: 29.6875
Iteration: 5061. Train Loss: 1.8188070058822632. Validation Accuracy: 28.125
Iteration: 5062. Train Loss: 1.6190646886825562. Validation Accuracy: 21.875
Iteration: 5063. Train Loss: 1.635481834411621. Validation Accuracy: 34.375
Iteration: 5064. Train Loss: 1.8272475004196167. Validation Accuracy: 32.8125
Iteration: 5065. Train Loss: 1.5982871055603027. Validation Accuracy: 21.875
Iteration: 5066. Train Loss: 1.6209325790405273. Validation Accuracy: 32.8125
Iteration: 5067. Train Loss: 1.5618056058883667. Validation Accuracy: 29.6875
Iteration: 5068. Train Loss: 1.6637271642684937. Validation Accuracy: 32.8125
Iteration: 5069. Train Loss: 1.6488049030303955. Validation Accuracy: 29.6875
Iteration: 5070. Train Loss: 1.8217003345489502. Validation Accuracy: 39.0625
Iteration: 5071. Train Loss: 1.6767410039901733. Validation Accuracy: 25.0
Iteration: 5072. Train Loss: 1.7320643663406372. Validation Accuracy: 34.375
Iteration: 5073. Train Loss: 1.9351069927215576. Validation Accuracy: 20.3125
Iteration: 5074. Train Loss: 1.5859421491622925. Validation Accuracy: 31.25
Iteration: 5075. Train Loss: 1.5578595399856567. Validation Accuracy: 29.6875
Iteration: 5076. Train Loss: 1.6095507144927979. Validation Accuracy: 23.4375
Iteration: 5077. Train Loss: 1.6242786645889282. Validation Accuracy: 23.4375
Iteration: 5078. Train Loss: 1.5947010517120361. Validation Accuracy: 28.125
Iteration: 5079. Train Loss: 1.6280066967010498. Validation Accuracy: 29.6875
Iteration: 5080. Train Loss: 1.8680922985076904. Validation Accuracy: 31.25
Iteration: 5081. Train Loss: 1.6677007675170898. Validation Accuracy: 23.4375
Iteration: 5082. Train Loss: 1.71925687789917. Validation Accuracy: 34.375
Iteration: 5083. Train Loss: 1.731764793395996. Validation Accuracy: 14.0625
Iteration: 5084. Train Loss: 1.7404897212982178. Validation Accuracy: 32.8125
Iteration: 5085. Train Loss: 1.5875763893127441. Validation Accuracy: 35.9375
Iteration: 5086. Train Loss: 1.6768287420272827. Validation Accuracy: 25.0
Iteration: 5087. Train Loss: 1.6131138801574707. Validation Accuracy: 21.875
Iteration: 5088. Train Loss: 1.7080419063568115. Validation Accuracy: 29.6875
Iteration: 5089. Train Loss: 1.601239562034607. Validation Accuracy: 29.6875
Iteration: 5090. Train Loss: 1.7075669765472412. Validation Accuracy: 28.125
Iteration: 5091. Train Loss: 1.7949566841125488. Validation Accuracy: 31.25
Iteration: 5092. Train Loss: 1.665088415145874. Validation Accuracy: 37.5
Iteration: 5093. Train Loss: 1.7027722597122192. Validation Accuracy: 23.4375
Iteration: 5094. Train Loss: 1.564322590827942. Validation Accuracy: 31.25
Iteration: 5095. Train Loss: 1.6192216873168945. Validation Accuracy: 21.875
Iteration: 5096. Train Loss: 1.7926905155181885. Validation Accuracy: 18.75
Iteration: 5097. Train Loss: 1.835263729095459. Validation Accuracy: 32.8125
Iteration: 5098. Train Loss: 1.6301158666610718. Validation Accuracy: 29.6875
Iteration: 5099. Train Loss: 1.7399992942810059. Validation Accuracy: 25.0
Iteration: 5100. Train Loss: 1.8160758018493652. Validation Accuracy: 32.8125
Iteration: 5101. Train Loss: 1.6290960311889648. Validation Accuracy: 31.25
Iteration: 5102. Train Loss: 1.7795499563217163. Validation Accuracy: 18.75
Iteration: 5103. Train Loss: 1.660054326057434. Validation Accuracy: 25.0
Iteration: 5104. Train Loss: 1.7300859689712524. Validation Accuracy: 34.375
Iteration: 5105. Train Loss: 1.7833116054534912. Validation Accuracy: 32.8125
Iteration: 5106. Train Loss: 1.6567084789276123. Validation Accuracy: 23.4375
Iteration: 5107. Train Loss: 1.5616474151611328. Validation Accuracy: 32.8125
Iteration: 5108. Train Loss: 1.5466243028640747. Validation Accuracy: 34.375
Iteration: 5109. Train Loss: 1.5421539545059204. Validation Accuracy: 37.5
Iteration: 5110. Train Loss: 1.6303958892822266. Validation Accuracy: 21.875
Iteration: 5111. Train Loss: 1.6603567600250244. Validation Accuracy: 26.5625
Iteration: 5112. Train Loss: 1.604018211364746. Validation Accuracy: 32.8125
Iteration: 5113. Train Loss: 1.7319612503051758. Validation Accuracy: 39.0625
Iteration: 5114. Train Loss: 1.7281663417816162. Validation Accuracy: 25.0
Iteration: 5115. Train Loss: 1.7257431745529175. Validation Accuracy: 28.125
Iteration: 5116. Train Loss: 1.5034526586532593. Validation Accuracy: 39.0625
Iteration: 5117. Train Loss: 1.614659309387207. Validation Accuracy: 31.25
Iteration: 5118. Train Loss: 1.7310434579849243. Validation Accuracy: 32.8125
Iteration: 5119. Train Loss: 1.7562296390533447. Validation Accuracy: 32.8125
Iteration: 5120. Train Loss: 1.7671548128128052. Validation Accuracy: 26.5625
Iteration: 5121. Train Loss: 1.7028138637542725. Validation Accuracy: 26.5625
Iteration: 5122. Train Loss: 1.678019404411316. Validation Accuracy: 31.25
Iteration: 5123. Train Loss: 1.6809464693069458. Validation Accuracy: 35.9375
Iteration: 5124. Train Loss: 1.8672316074371338. Validation Accuracy: 23.4375
Iteration: 5125. Train Loss: 1.6720823049545288. Validation Accuracy: 34.375
Iteration: 5126. Train Loss: 1.7018033266067505. Validation Accuracy: 26.5625
Iteration: 5127. Train Loss: 1.616794466972351. Validation Accuracy: 25.0
Iteration: 5128. Train Loss: 1.5285215377807617. Validation Accuracy: 29.6875
Iteration: 5129. Train Loss: 1.6799654960632324. Validation Accuracy: 23.4375
Iteration: 5130. Train Loss: 1.7086689472198486. Validation Accuracy: 28.125
Iteration: 5131. Train Loss: 1.665612816810608. Validation Accuracy: 43.75
Iteration: 5132. Train Loss: 1.7415461540222168. Validation Accuracy: 32.8125
Iteration: 5133. Train Loss: 1.643828272819519. Validation Accuracy: 31.25
Iteration: 5134. Train Loss: 1.506395697593689. Validation Accuracy: 35.9375
Iteration: 5135. Train Loss: 1.4880590438842773. Validation Accuracy: 34.375
Iteration: 5136. Train Loss: 1.6890642642974854. Validation Accuracy: 32.8125
Iteration: 5137. Train Loss: 1.671212911605835. Validation Accuracy: 35.9375
Iteration: 5138. Train Loss: 1.7569011449813843. Validation Accuracy: 34.375
Iteration: 5139. Train Loss: 1.6786819696426392. Validation Accuracy: 29.6875
Iteration: 5140. Train Loss: 1.6825230121612549. Validation Accuracy: 35.9375
Iteration: 5141. Train Loss: 1.6986210346221924. Validation Accuracy: 29.6875
Iteration: 5142. Train Loss: 1.713042140007019. Validation Accuracy: 21.875
Iteration: 5143. Train Loss: 1.7709133625030518. Validation Accuracy: 37.5
Iteration: 5144. Train Loss: 1.6919807195663452. Validation Accuracy: 26.5625
Iteration: 5145. Train Loss: 1.684194564819336. Validation Accuracy: 23.4375
Iteration: 5146. Train Loss: 1.7112982273101807. Validation Accuracy: 26.5625
Iteration: 5147. Train Loss: 1.6038727760314941. Validation Accuracy: 32.8125
Iteration: 5148. Train Loss: 1.6885931491851807. Validation Accuracy: 29.6875
Iteration: 5149. Train Loss: 1.7333332300186157. Validation Accuracy: 26.5625
Iteration: 5150. Train Loss: 1.7728551626205444. Validation Accuracy: 40.625
Iteration: 5151. Train Loss: 1.7307108640670776. Validation Accuracy: 29.6875
Iteration: 5152. Train Loss: 1.6785823106765747. Validation Accuracy: 21.875
Iteration: 5153. Train Loss: 1.790657639503479. Validation Accuracy: 23.4375
Iteration: 5154. Train Loss: 1.5714747905731201. Validation Accuracy: 35.9375
Iteration: 5155. Train Loss: 1.6760375499725342. Validation Accuracy: 25.0
Iteration: 5156. Train Loss: 1.5153555870056152. Validation Accuracy: 28.125
Iteration: 5157. Train Loss: 1.7694957256317139. Validation Accuracy: 20.3125
Iteration: 5158. Train Loss: 1.7132459878921509. Validation Accuracy: 39.0625
Iteration: 5159. Train Loss: 1.565083622932434. Validation Accuracy: 31.25
Iteration: 5160. Train Loss: 1.7739962339401245. Validation Accuracy: 28.125
Iteration: 5161. Train Loss: 1.6993522644042969. Validation Accuracy: 31.25
Iteration: 5162. Train Loss: 1.6335026025772095. Validation Accuracy: 29.6875
Iteration: 5163. Train Loss: 1.747950792312622. Validation Accuracy: 39.0625
Iteration: 5164. Train Loss: 1.9420959949493408. Validation Accuracy: 32.8125
Iteration: 5165. Train Loss: 1.674635648727417. Validation Accuracy: 32.8125
Iteration: 5166. Train Loss: 1.6542218923568726. Validation Accuracy: 25.0
Iteration: 5167. Train Loss: 1.6095577478408813. Validation Accuracy: 15.625
Iteration: 5168. Train Loss: 1.8092912435531616. Validation Accuracy: 39.0625
Iteration: 5169. Train Loss: 1.5556647777557373. Validation Accuracy: 31.25
Iteration: 5170. Train Loss: 1.6091722249984741. Validation Accuracy: 35.9375
Iteration: 5171. Train Loss: 1.716558814048767. Validation Accuracy: 29.6875
Iteration: 5172. Train Loss: 1.5844688415527344. Validation Accuracy: 23.4375
Iteration: 5173. Train Loss: 1.6373308897018433. Validation Accuracy: 25.0
Iteration: 5174. Train Loss: 1.6733253002166748. Validation Accuracy: 28.125
Iteration: 5175. Train Loss: 1.7431977987289429. Validation Accuracy: 21.875
Iteration: 5176. Train Loss: 1.6308938264846802. Validation Accuracy: 29.6875
Iteration: 5177. Train Loss: 1.889090657234192. Validation Accuracy: 34.375
Iteration: 5178. Train Loss: 1.6977108716964722. Validation Accuracy: 29.6875
Iteration: 5179. Train Loss: 1.5690199136734009. Validation Accuracy: 31.25
Iteration: 5180. Train Loss: 1.8155484199523926. Validation Accuracy: 31.25
Iteration: 5181. Train Loss: 1.6331387758255005. Validation Accuracy: 31.25
Iteration: 5182. Train Loss: 1.5937106609344482. Validation Accuracy: 25.0
Iteration: 5183. Train Loss: 1.7334569692611694. Validation Accuracy: 32.8125
Iteration: 5184. Train Loss: 1.5944277048110962. Validation Accuracy: 23.4375
Iteration: 5185. Train Loss: 1.545116901397705. Validation Accuracy: 26.5625
Iteration: 5186. Train Loss: 1.5291121006011963. Validation Accuracy: 17.1875
Iteration: 5187. Train Loss: 1.7211170196533203. Validation Accuracy: 21.875
Iteration: 5188. Train Loss: 1.9196268320083618. Validation Accuracy: 25.0
Iteration: 5189. Train Loss: 1.593553900718689. Validation Accuracy: 35.9375
Iteration: 5190. Train Loss: 1.7323071956634521. Validation Accuracy: 9.375
Iteration: 5191. Train Loss: 1.672442078590393. Validation Accuracy: 28.125
Iteration: 5192. Train Loss: 1.8003202676773071. Validation Accuracy: 40.625
Iteration: 5193. Train Loss: 1.672270655632019. Validation Accuracy: 32.8125
Iteration: 5194. Train Loss: 1.6530568599700928. Validation Accuracy: 37.5
Iteration: 5195. Train Loss: 1.681038737297058. Validation Accuracy: 40.625
Iteration: 5196. Train Loss: 1.733906865119934. Validation Accuracy: 23.4375
Iteration: 5197. Train Loss: 1.6582821607589722. Validation Accuracy: 32.8125
Iteration: 5198. Train Loss: 1.6563818454742432. Validation Accuracy: 23.4375
Iteration: 5199. Train Loss: 1.7892619371414185. Validation Accuracy: 31.25
Iteration: 5200. Train Loss: 1.8820481300354004. Validation Accuracy: 28.125
Iteration: 5201. Train Loss: 1.6615946292877197. Validation Accuracy: 35.9375
Iteration: 5202. Train Loss: 1.7070021629333496. Validation Accuracy: 31.25
Iteration: 5203. Train Loss: 1.6888234615325928. Validation Accuracy: 26.5625
Iteration: 5204. Train Loss: 1.5920493602752686. Validation Accuracy: 42.1875
Iteration: 5205. Train Loss: 1.6453676223754883. Validation Accuracy: 26.5625
Iteration: 5206. Train Loss: 1.7343953847885132. Validation Accuracy: 31.25
Iteration: 5207. Train Loss: 1.470300555229187. Validation Accuracy: 21.875
Iteration: 5208. Train Loss: 1.5631219148635864. Validation Accuracy: 26.5625
Iteration: 5209. Train Loss: 1.6871787309646606. Validation Accuracy: 43.75
Iteration: 5210. Train Loss: 1.759813666343689. Validation Accuracy: 21.875
Iteration: 5211. Train Loss: 1.6754369735717773. Validation Accuracy: 34.375
Iteration: 5212. Train Loss: 1.6437807083129883. Validation Accuracy: 29.6875
Iteration: 5213. Train Loss: 1.615448236465454. Validation Accuracy: 29.6875
Iteration: 5214. Train Loss: 1.8181232213974. Validation Accuracy: 37.5
Iteration: 5215. Train Loss: 1.7235037088394165. Validation Accuracy: 26.5625
Iteration: 5216. Train Loss: 1.6907671689987183. Validation Accuracy: 31.25
Iteration: 5217. Train Loss: 1.7905139923095703. Validation Accuracy: 28.125
Iteration: 5218. Train Loss: 1.6266192197799683. Validation Accuracy: 31.25
Iteration: 5219. Train Loss: 1.7947874069213867. Validation Accuracy: 42.1875
Iteration: 5220. Train Loss: 1.7412008047103882. Validation Accuracy: 10.9375
Iteration: 5221. Train Loss: 1.681975245475769. Validation Accuracy: 21.875
Iteration: 5222. Train Loss: 1.6709675788879395. Validation Accuracy: 26.5625
Iteration: 5223. Train Loss: 1.863254189491272. Validation Accuracy: 28.125
Iteration: 5224. Train Loss: 1.7669328451156616. Validation Accuracy: 37.5
Iteration: 5225. Train Loss: 1.805863857269287. Validation Accuracy: 23.4375
Iteration: 5226. Train Loss: 1.854685664176941. Validation Accuracy: 23.4375
Iteration: 5227. Train Loss: 1.6260477304458618. Validation Accuracy: 28.125
Iteration: 5228. Train Loss: 1.6037672758102417. Validation Accuracy: 25.0
Iteration: 5229. Train Loss: 1.727607250213623. Validation Accuracy: 43.75
Iteration: 5230. Train Loss: 1.561808466911316. Validation Accuracy: 18.75
Iteration: 5231. Train Loss: 1.7724839448928833. Validation Accuracy: 34.375
Iteration: 5232. Train Loss: 1.7084004878997803. Validation Accuracy: 23.4375
Iteration: 5233. Train Loss: 1.7620823383331299. Validation Accuracy: 18.75
Iteration: 5234. Train Loss: 1.711220383644104. Validation Accuracy: 32.8125
Iteration: 5235. Train Loss: 1.649312973022461. Validation Accuracy: 34.375
Iteration: 5236. Train Loss: 1.7023735046386719. Validation Accuracy: 26.5625
Iteration: 5237. Train Loss: 1.6233344078063965. Validation Accuracy: 26.5625
Iteration: 5238. Train Loss: 1.7094091176986694. Validation Accuracy: 32.8125
Iteration: 5239. Train Loss: 1.8364547491073608. Validation Accuracy: 29.6875
Iteration: 5240. Train Loss: 1.7394475936889648. Validation Accuracy: 23.4375
Iteration: 5241. Train Loss: 1.8012287616729736. Validation Accuracy: 25.0
Iteration: 5242. Train Loss: 1.6382172107696533. Validation Accuracy: 26.5625
Iteration: 5243. Train Loss: 1.618886113166809. Validation Accuracy: 26.5625
Iteration: 5244. Train Loss: 1.7837026119232178. Validation Accuracy: 26.5625
Iteration: 5245. Train Loss: 1.6294569969177246. Validation Accuracy: 23.4375
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Iteration: 9856. Train Loss: 1.474153995513916. Validation Accuracy: 29.6875
Iteration: 9857. Train Loss: 1.537128210067749. Validation Accuracy: 29.6875
Iteration: 9858. Train Loss: 1.5991554260253906. Validation Accuracy: 42.1875
Iteration: 9859. Train Loss: 1.4955016374588013. Validation Accuracy: 29.6875
Iteration: 9860. Train Loss: 1.5330816507339478. Validation Accuracy: 20.3125
Iteration: 9861. Train Loss: 1.497058391571045. Validation Accuracy: 25.0
Iteration: 9862. Train Loss: 1.5235369205474854. Validation Accuracy: 35.9375
Iteration: 9863. Train Loss: 1.6332147121429443. Validation Accuracy: 42.1875
Iteration: 9864. Train Loss: 1.5155609846115112. Validation Accuracy: 25.0
Iteration: 9865. Train Loss: 1.5997484922409058. Validation Accuracy: 31.25
Iteration: 9866. Train Loss: 1.4356589317321777. Validation Accuracy: 23.4375
Iteration: 9867. Train Loss: 1.4335968494415283. Validation Accuracy: 32.8125
Iteration: 9868. Train Loss: 1.5163902044296265. Validation Accuracy: 20.3125
Iteration: 9869. Train Loss: 1.5283254384994507. Validation Accuracy: 25.0
Iteration: 9870. Train Loss: 1.5516200065612793. Validation Accuracy: 28.125
Iteration: 9871. Train Loss: 1.6316478252410889. Validation Accuracy: 34.375
Iteration: 9872. Train Loss: 1.4981993436813354. Validation Accuracy: 32.8125
Iteration: 9873. Train Loss: 1.5509639978408813. Validation Accuracy: 32.8125
Iteration: 9874. Train Loss: 1.4072504043579102. Validation Accuracy: 28.125
Iteration: 9875. Train Loss: 1.5112637281417847. Validation Accuracy: 28.125
Iteration: 9876. Train Loss: 1.353181004524231. Validation Accuracy: 29.6875
Iteration: 9877. Train Loss: 1.464714765548706. Validation Accuracy: 21.875
Iteration: 9878. Train Loss: 1.5166057348251343. Validation Accuracy: 29.6875
Iteration: 9879. Train Loss: 1.5260099172592163. Validation Accuracy: 25.0
Iteration: 9880. Train Loss: 1.5011154413223267. Validation Accuracy: 28.125
Iteration: 9881. Train Loss: 1.521499752998352. Validation Accuracy: 28.125
Iteration: 9882. Train Loss: 1.4749364852905273. Validation Accuracy: 28.125
Iteration: 9883. Train Loss: 1.639136552810669. Validation Accuracy: 35.9375
Iteration: 9884. Train Loss: 1.4320693016052246. Validation Accuracy: 31.25
Iteration: 9885. Train Loss: 1.5347243547439575. Validation Accuracy: 18.75
Iteration: 9886. Train Loss: 1.5503982305526733. Validation Accuracy: 37.5
Iteration: 9887. Train Loss: 1.461388111114502. Validation Accuracy: 32.8125
Iteration: 9888. Train Loss: 1.549534797668457. Validation Accuracy: 31.25
Iteration: 9889. Train Loss: 1.5103636980056763. Validation Accuracy: 35.9375
Iteration: 9890. Train Loss: 1.5931795835494995. Validation Accuracy: 35.9375
Iteration: 9891. Train Loss: 1.6603745222091675. Validation Accuracy: 28.125
Iteration: 9892. Train Loss: 1.6475681066513062. Validation Accuracy: 29.6875
Iteration: 9893. Train Loss: 1.6175850629806519. Validation Accuracy: 29.6875
Iteration: 9894. Train Loss: 1.552539587020874. Validation Accuracy: 35.9375
Iteration: 9895. Train Loss: 1.5247948169708252. Validation Accuracy: 29.6875
Iteration: 9896. Train Loss: 1.467331051826477. Validation Accuracy: 31.25
Iteration: 9897. Train Loss: 1.4567865133285522. Validation Accuracy: 29.6875
Iteration: 9898. Train Loss: 1.5036749839782715. Validation Accuracy: 28.125
Iteration: 9899. Train Loss: 1.4600348472595215. Validation Accuracy: 35.9375
Iteration: 9900. Train Loss: 1.566024899482727. Validation Accuracy: 25.0
Iteration: 9901. Train Loss: 1.6359659433364868. Validation Accuracy: 25.0
Iteration: 9902. Train Loss: 1.3370459079742432. Validation Accuracy: 26.5625
Iteration: 9903. Train Loss: 1.4036277532577515. Validation Accuracy: 20.3125
Iteration: 9904. Train Loss: 1.5321675539016724. Validation Accuracy: 40.625
Iteration: 9905. Train Loss: 1.7141239643096924. Validation Accuracy: 26.5625
Iteration: 9906. Train Loss: 1.7217373847961426. Validation Accuracy: 17.1875
Iteration: 9907. Train Loss: 1.5407735109329224. Validation Accuracy: 23.4375
Iteration: 9908. Train Loss: 1.4534966945648193. Validation Accuracy: 37.5
Iteration: 9909. Train Loss: 1.4996986389160156. Validation Accuracy: 34.375
Iteration: 9910. Train Loss: 1.6024037599563599. Validation Accuracy: 25.0
Iteration: 9911. Train Loss: 1.3702126741409302. Validation Accuracy: 29.6875
Iteration: 9912. Train Loss: 1.618051528930664. Validation Accuracy: 31.25
Iteration: 9913. Train Loss: 1.6214367151260376. Validation Accuracy: 32.8125
Iteration: 9914. Train Loss: 1.5447278022766113. Validation Accuracy: 25.0
Iteration: 9915. Train Loss: 1.4578701257705688. Validation Accuracy: 29.6875
Iteration: 9916. Train Loss: 1.6104321479797363. Validation Accuracy: 21.875
Iteration: 9917. Train Loss: 1.8365997076034546. Validation Accuracy: 25.0
Iteration: 9918. Train Loss: 1.4964197874069214. Validation Accuracy: 23.4375
Iteration: 9919. Train Loss: 1.596856713294983. Validation Accuracy: 28.125
Iteration: 9920. Train Loss: 1.5200740098953247. Validation Accuracy: 35.9375
Iteration: 9921. Train Loss: 1.5957605838775635. Validation Accuracy: 25.0
Iteration: 9922. Train Loss: 1.5244660377502441. Validation Accuracy: 28.125
Iteration: 9923. Train Loss: 1.8179486989974976. Validation Accuracy: 25.0
Iteration: 9924. Train Loss: 1.47443687915802. Validation Accuracy: 29.6875
Iteration: 9925. Train Loss: 1.6776152849197388. Validation Accuracy: 34.375
Iteration: 9926. Train Loss: 1.5783960819244385. Validation Accuracy: 26.5625
Iteration: 9927. Train Loss: 1.4817882776260376. Validation Accuracy: 18.75
Iteration: 9928. Train Loss: 1.6340514421463013. Validation Accuracy: 34.375
Iteration: 9929. Train Loss: 1.6204960346221924. Validation Accuracy: 25.0
Iteration: 9930. Train Loss: 1.622406244277954. Validation Accuracy: 32.8125
Iteration: 9931. Train Loss: 1.467696189880371. Validation Accuracy: 23.4375
Iteration: 9932. Train Loss: 1.5290364027023315. Validation Accuracy: 31.25
Iteration: 9933. Train Loss: 1.646046757698059. Validation Accuracy: 25.0
Iteration: 9934. Train Loss: 1.3967902660369873. Validation Accuracy: 32.8125
Iteration: 9935. Train Loss: 1.6758357286453247. Validation Accuracy: 35.9375
Iteration: 9936. Train Loss: 1.5038186311721802. Validation Accuracy: 25.0
Iteration: 9937. Train Loss: 1.4972108602523804. Validation Accuracy: 34.375
Iteration: 9938. Train Loss: 1.547987461090088. Validation Accuracy: 17.1875
Iteration: 9939. Train Loss: 1.66202974319458. Validation Accuracy: 26.5625
Iteration: 9940. Train Loss: 1.4109158515930176. Validation Accuracy: 21.875
Iteration: 9941. Train Loss: 1.4392098188400269. Validation Accuracy: 23.4375
Iteration: 9942. Train Loss: 1.6148335933685303. Validation Accuracy: 25.0
Iteration: 9943. Train Loss: 1.429321050643921. Validation Accuracy: 25.0
Iteration: 9944. Train Loss: 1.4130816459655762. Validation Accuracy: 35.9375
Iteration: 9945. Train Loss: 1.633038878440857. Validation Accuracy: 25.0
Iteration: 9946. Train Loss: 1.494587779045105. Validation Accuracy: 26.5625
Iteration: 9947. Train Loss: 1.5240360498428345. Validation Accuracy: 28.125
Iteration: 9948. Train Loss: 1.5037983655929565. Validation Accuracy: 46.875
Iteration: 9949. Train Loss: 1.6559102535247803. Validation Accuracy: 34.375
Iteration: 9950. Train Loss: 1.5378504991531372. Validation Accuracy: 29.6875
Iteration: 9951. Train Loss: 1.5051155090332031. Validation Accuracy: 34.375
Iteration: 9952. Train Loss: 1.596400260925293. Validation Accuracy: 28.125
Iteration: 9953. Train Loss: 1.7236669063568115. Validation Accuracy: 42.1875
Iteration: 9954. Train Loss: 1.479715347290039. Validation Accuracy: 20.3125
Iteration: 9955. Train Loss: 1.5084482431411743. Validation Accuracy: 26.5625
Iteration: 9956. Train Loss: 1.425469160079956. Validation Accuracy: 26.5625
Iteration: 9957. Train Loss: 1.4750419855117798. Validation Accuracy: 20.3125
Iteration: 9958. Train Loss: 1.5993726253509521. Validation Accuracy: 26.5625
Iteration: 9959. Train Loss: 1.5982037782669067. Validation Accuracy: 34.375
Iteration: 9960. Train Loss: 1.5242908000946045. Validation Accuracy: 25.0
Iteration: 9961. Train Loss: 1.5427173376083374. Validation Accuracy: 35.9375
Iteration: 9962. Train Loss: 1.5997053384780884. Validation Accuracy: 26.5625
Iteration: 9963. Train Loss: 1.4844098091125488. Validation Accuracy: 35.9375
Iteration: 9964. Train Loss: 1.598536729812622. Validation Accuracy: 23.4375
Iteration: 9965. Train Loss: 1.5851157903671265. Validation Accuracy: 39.0625
Iteration: 9966. Train Loss: 1.650887370109558. Validation Accuracy: 29.6875
Iteration: 9967. Train Loss: 1.5583745241165161. Validation Accuracy: 34.375
Iteration: 9968. Train Loss: 1.5085448026657104. Validation Accuracy: 32.8125
Iteration: 9969. Train Loss: 1.5401663780212402. Validation Accuracy: 29.6875
Iteration: 9970. Train Loss: 1.5117874145507812. Validation Accuracy: 17.1875
Iteration: 9971. Train Loss: 1.6490329504013062. Validation Accuracy: 26.5625
Iteration: 9972. Train Loss: 1.4529516696929932. Validation Accuracy: 17.1875
Iteration: 9973. Train Loss: 1.332730770111084. Validation Accuracy: 31.25
Iteration: 9974. Train Loss: 1.4897661209106445. Validation Accuracy: 39.0625
Iteration: 9975. Train Loss: 1.4698249101638794. Validation Accuracy: 34.375
Iteration: 9976. Train Loss: 1.5949302911758423. Validation Accuracy: 26.5625
Iteration: 9977. Train Loss: 1.5107784271240234. Validation Accuracy: 14.0625
Iteration: 9978. Train Loss: 1.5967844724655151. Validation Accuracy: 26.5625
Iteration: 9979. Train Loss: 1.434525966644287. Validation Accuracy: 29.6875
Iteration: 9980. Train Loss: 1.4936028718948364. Validation Accuracy: 32.8125
Iteration: 9981. Train Loss: 1.623683214187622. Validation Accuracy: 29.6875
Iteration: 9982. Train Loss: 1.5481349229812622. Validation Accuracy: 39.0625
Iteration: 9983. Train Loss: 1.5078004598617554. Validation Accuracy: 46.875
Iteration: 9984. Train Loss: 1.47972571849823. Validation Accuracy: 21.875
Iteration: 9985. Train Loss: 1.5602668523788452. Validation Accuracy: 34.375
Iteration: 9986. Train Loss: 1.5716227293014526. Validation Accuracy: 26.5625
Iteration: 9987. Train Loss: 1.4307472705841064. Validation Accuracy: 26.5625
Iteration: 9988. Train Loss: 1.5921651124954224. Validation Accuracy: 29.6875
Iteration: 9989. Train Loss: 1.5716463327407837. Validation Accuracy: 26.5625
Iteration: 9990. Train Loss: 1.5684117078781128. Validation Accuracy: 29.6875
Iteration: 9991. Train Loss: 1.5631777048110962. Validation Accuracy: 37.5
Iteration: 9992. Train Loss: 1.5060664415359497. Validation Accuracy: 35.9375
Iteration: 9993. Train Loss: 1.474847435951233. Validation Accuracy: 26.5625
Iteration: 9994. Train Loss: 1.668184518814087. Validation Accuracy: 40.625
Iteration: 9995. Train Loss: 1.3570401668548584. Validation Accuracy: 28.125
Iteration: 9996. Train Loss: 1.7487785816192627. Validation Accuracy: 32.8125
Iteration: 9997. Train Loss: 1.506394624710083. Validation Accuracy: 25.0
Iteration: 9998. Train Loss: 1.4273601770401. Validation Accuracy: 34.375
Iteration: 9999. Train Loss: 1.5282505750656128. Validation Accuracy: 34.375
Finished Training
Learning curves¶
In [ ]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [ ]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Performance on the Test set¶
In [ ]:
L = random.randint(3, 10)
_, inputs, labels = get_batch(K, L, pos = pos)
# Forward pass only to get logits/output
outputs = model(inputs)
test_cost = criterion(outputs, labels)
print("Cost function value: " + str(test_cost.item()))
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
print("Accuracy: " + str(accuracy_score(labels.numpy(), predicted.numpy())))
print("Classification Report: \n")
print(classification_report(labels.numpy(), predicted.numpy(), zero_division = 0))
print("Confusion Matrix: \n")
cm = confusion_matrix(labels.numpy(), predicted.numpy(), labels = range(10))
disp = ConfusionMatrixDisplay(confusion_matrix = cm,
display_labels = range(10))
disp.plot()
plt.show()
Cost function value: 1.4310827255249023
Accuracy: 0.390625
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 8
1 0.00 0.00 0.00 10
2 0.36 1.00 0.53 5
3 0.00 0.00 0.00 3
4 0.00 0.00 0.00 3
5 1.00 1.00 1.00 10
6 0.00 0.00 0.00 3
7 0.25 1.00 0.40 10
8 0.00 0.00 0.00 7
9 0.00 0.00 0.00 5
accuracy 0.39 64
macro avg 0.16 0.30 0.19 64
weighted avg 0.22 0.39 0.26 64
Confusion Matrix:
Hidden state size = 5¶
In [ ]:
INPUT_DIM = 10
HIDDEN_DIM = 5
OUTPUT_DIM = 10
NUM_LAYERS = 5
LR = 0.001
K = 64
pos = 1
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [ ]:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [ ]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for iteration in range(10*1000):
L = random.randint(3, 10)
_, inputs, labels = get_batch(K, L, pos = pos)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
_, inputs, labels = get_batch(K, L, pos = pos)
# Forward pass only to get logits/output
outputs = model(inputs)
val_cost = criterion(outputs, labels)
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(iteration, loss.item(), accuracy))
print('Finished Training')
Streaming output truncated to the last 5000 lines.
Iteration: 5001. Train Loss: 1.3653175830841064. Validation Accuracy: 34.375
Iteration: 5002. Train Loss: 1.4070852994918823. Validation Accuracy: 34.375
Iteration: 5003. Train Loss: 1.415418028831482. Validation Accuracy: 39.0625
Iteration: 5004. Train Loss: 1.4358775615692139. Validation Accuracy: 35.9375
Iteration: 5005. Train Loss: 1.3776018619537354. Validation Accuracy: 43.75
Iteration: 5006. Train Loss: 1.219407320022583. Validation Accuracy: 35.9375
Iteration: 5007. Train Loss: 1.458021879196167. Validation Accuracy: 31.25
Iteration: 5008. Train Loss: 1.3509496450424194. Validation Accuracy: 48.4375
Iteration: 5009. Train Loss: 1.2922574281692505. Validation Accuracy: 42.1875
Iteration: 5010. Train Loss: 1.302193522453308. Validation Accuracy: 37.5
Iteration: 5011. Train Loss: 1.4463647603988647. Validation Accuracy: 39.0625
Iteration: 5012. Train Loss: 1.395965337753296. Validation Accuracy: 42.1875
Iteration: 5013. Train Loss: 1.398197054862976. Validation Accuracy: 35.9375
Iteration: 5014. Train Loss: 1.29208242893219. Validation Accuracy: 40.625
Iteration: 5015. Train Loss: 1.3469915390014648. Validation Accuracy: 39.0625
Iteration: 5016. Train Loss: 1.2952964305877686. Validation Accuracy: 40.625
Iteration: 5017. Train Loss: 1.336851954460144. Validation Accuracy: 28.125
Iteration: 5018. Train Loss: 1.202841877937317. Validation Accuracy: 45.3125
Iteration: 5019. Train Loss: 1.190977931022644. Validation Accuracy: 51.5625
Iteration: 5020. Train Loss: 1.5751670598983765. Validation Accuracy: 29.6875
Iteration: 5021. Train Loss: 1.3472192287445068. Validation Accuracy: 37.5
Iteration: 5022. Train Loss: 1.3846817016601562. Validation Accuracy: 43.75
Iteration: 5023. Train Loss: 1.4165338277816772. Validation Accuracy: 40.625
Iteration: 5024. Train Loss: 1.3090851306915283. Validation Accuracy: 35.9375
Iteration: 5025. Train Loss: 1.3238019943237305. Validation Accuracy: 32.8125
Iteration: 5026. Train Loss: 1.273673415184021. Validation Accuracy: 43.75
Iteration: 5027. Train Loss: 1.2768133878707886. Validation Accuracy: 39.0625
Iteration: 5028. Train Loss: 1.358961582183838. Validation Accuracy: 40.625
Iteration: 5029. Train Loss: 1.4111874103546143. Validation Accuracy: 40.625
Iteration: 5030. Train Loss: 1.3489038944244385. Validation Accuracy: 37.5
Iteration: 5031. Train Loss: 1.357507348060608. Validation Accuracy: 46.875
Iteration: 5032. Train Loss: 1.2888972759246826. Validation Accuracy: 42.1875
Iteration: 5033. Train Loss: 1.286661982536316. Validation Accuracy: 45.3125
Iteration: 5034. Train Loss: 1.3476673364639282. Validation Accuracy: 39.0625
Iteration: 5035. Train Loss: 1.3062409162521362. Validation Accuracy: 45.3125
Iteration: 5036. Train Loss: 1.319455862045288. Validation Accuracy: 20.3125
Iteration: 5037. Train Loss: 1.4918628931045532. Validation Accuracy: 31.25
Iteration: 5038. Train Loss: 1.4383068084716797. Validation Accuracy: 53.125
Iteration: 5039. Train Loss: 1.4030969142913818. Validation Accuracy: 50.0
Iteration: 5040. Train Loss: 1.2928521633148193. Validation Accuracy: 29.6875
Iteration: 5041. Train Loss: 1.3789550065994263. Validation Accuracy: 42.1875
Iteration: 5042. Train Loss: 1.2366667985916138. Validation Accuracy: 45.3125
Iteration: 5043. Train Loss: 1.285007119178772. Validation Accuracy: 45.3125
Iteration: 5044. Train Loss: 1.3020474910736084. Validation Accuracy: 42.1875
Iteration: 5045. Train Loss: 1.5035563707351685. Validation Accuracy: 40.625
Iteration: 5046. Train Loss: 1.4616962671279907. Validation Accuracy: 39.0625
Iteration: 5047. Train Loss: 1.324016809463501. Validation Accuracy: 39.0625
Iteration: 5048. Train Loss: 1.3148750066757202. Validation Accuracy: 43.75
Iteration: 5049. Train Loss: 1.258792757987976. Validation Accuracy: 34.375
Iteration: 5050. Train Loss: 1.3300762176513672. Validation Accuracy: 43.75
Iteration: 5051. Train Loss: 1.2289849519729614. Validation Accuracy: 39.0625
Iteration: 5052. Train Loss: 1.2738614082336426. Validation Accuracy: 34.375
Iteration: 5053. Train Loss: 1.2392311096191406. Validation Accuracy: 37.5
Iteration: 5054. Train Loss: 1.2827742099761963. Validation Accuracy: 37.5
Iteration: 5055. Train Loss: 1.2947720289230347. Validation Accuracy: 46.875
Iteration: 5056. Train Loss: 1.393784761428833. Validation Accuracy: 35.9375
Iteration: 5057. Train Loss: 1.441131830215454. Validation Accuracy: 39.0625
Iteration: 5058. Train Loss: 1.2694329023361206. Validation Accuracy: 32.8125
Iteration: 5059. Train Loss: 1.3029884099960327. Validation Accuracy: 31.25
Iteration: 5060. Train Loss: 1.2223267555236816. Validation Accuracy: 42.1875
Iteration: 5061. Train Loss: 1.3621400594711304. Validation Accuracy: 48.4375
Iteration: 5062. Train Loss: 1.2897175550460815. Validation Accuracy: 35.9375
Iteration: 5063. Train Loss: 1.266332745552063. Validation Accuracy: 53.125
Iteration: 5064. Train Loss: 1.3309416770935059. Validation Accuracy: 35.9375
Iteration: 5065. Train Loss: 1.2486591339111328. Validation Accuracy: 35.9375
Iteration: 5066. Train Loss: 1.3524075746536255. Validation Accuracy: 39.0625
Iteration: 5067. Train Loss: 1.1562196016311646. Validation Accuracy: 37.5
Iteration: 5068. Train Loss: 1.4490076303482056. Validation Accuracy: 43.75
Iteration: 5069. Train Loss: 1.2028111219406128. Validation Accuracy: 39.0625
Iteration: 5070. Train Loss: 1.2553743124008179. Validation Accuracy: 34.375
Iteration: 5071. Train Loss: 1.2752866744995117. Validation Accuracy: 40.625
Iteration: 5072. Train Loss: 1.386479377746582. Validation Accuracy: 35.9375
Iteration: 5073. Train Loss: 1.3826793432235718. Validation Accuracy: 35.9375
Iteration: 5074. Train Loss: 1.2004283666610718. Validation Accuracy: 35.9375
Iteration: 5075. Train Loss: 1.3575658798217773. Validation Accuracy: 48.4375
Iteration: 5076. Train Loss: 1.421629786491394. Validation Accuracy: 43.75
Iteration: 5077. Train Loss: 1.3319733142852783. Validation Accuracy: 45.3125
Iteration: 5078. Train Loss: 1.1725636720657349. Validation Accuracy: 40.625
Iteration: 5079. Train Loss: 1.254848837852478. Validation Accuracy: 42.1875
Iteration: 5080. Train Loss: 1.3484777212142944. Validation Accuracy: 50.0
Iteration: 5081. Train Loss: 1.2863407135009766. Validation Accuracy: 39.0625
Iteration: 5082. Train Loss: 1.1784560680389404. Validation Accuracy: 39.0625
Iteration: 5083. Train Loss: 1.2970974445343018. Validation Accuracy: 46.875
Iteration: 5084. Train Loss: 1.2592014074325562. Validation Accuracy: 45.3125
Iteration: 5085. Train Loss: 1.3013430833816528. Validation Accuracy: 42.1875
Iteration: 5086. Train Loss: 1.2893391847610474. Validation Accuracy: 43.75
Iteration: 5087. Train Loss: 1.3229426145553589. Validation Accuracy: 40.625
Iteration: 5088. Train Loss: 1.2620477676391602. Validation Accuracy: 32.8125
Iteration: 5089. Train Loss: 1.2608349323272705. Validation Accuracy: 29.6875
Iteration: 5090. Train Loss: 1.3705052137374878. Validation Accuracy: 45.3125
Iteration: 5091. Train Loss: 1.2870022058486938. Validation Accuracy: 39.0625
Iteration: 5092. Train Loss: 1.366623044013977. Validation Accuracy: 51.5625
Iteration: 5093. Train Loss: 1.2717312574386597. Validation Accuracy: 32.8125
Iteration: 5094. Train Loss: 1.3550115823745728. Validation Accuracy: 42.1875
Iteration: 5095. Train Loss: 1.3664746284484863. Validation Accuracy: 35.9375
Iteration: 5096. Train Loss: 1.3624515533447266. Validation Accuracy: 31.25
Iteration: 5097. Train Loss: 1.3482000827789307. Validation Accuracy: 39.0625
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Iteration: 9993. Train Loss: 1.2897807359695435. Validation Accuracy: 48.4375
Iteration: 9994. Train Loss: 1.3293907642364502. Validation Accuracy: 39.0625
Iteration: 9995. Train Loss: 1.1658148765563965. Validation Accuracy: 51.5625
Iteration: 9996. Train Loss: 1.1387901306152344. Validation Accuracy: 46.875
Iteration: 9997. Train Loss: 1.0888102054595947. Validation Accuracy: 45.3125
Iteration: 9998. Train Loss: 1.0263197422027588. Validation Accuracy: 35.9375
Iteration: 9999. Train Loss: 1.28097665309906. Validation Accuracy: 45.3125
Finished Training
Learning curves¶
In [ ]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [ ]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Performance on the Test set¶
In [ ]:
L = random.randint(3, 10)
_, inputs, labels = get_batch(K, L, pos = pos)
# Forward pass only to get logits/output
outputs = model(inputs)
test_cost = criterion(outputs, labels)
print("Cost function value: " + str(test_cost.item()))
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
print("Accuracy: " + str(accuracy_score(labels.numpy(), predicted.numpy())))
print("Classification Report: \n")
print(classification_report(labels.numpy(), predicted.numpy(), zero_division = 0))
print("Confusion Matrix: \n")
cm = confusion_matrix(labels.numpy(), predicted.numpy(), labels = range(10))
disp = ConfusionMatrixDisplay(confusion_matrix = cm,
display_labels = range(10))
disp.plot()
plt.show()
Cost function value: 1.2263067960739136
Accuracy: 0.359375
Classification Report:
precision recall f1-score support
0 1.00 1.00 1.00 6
1 0.00 0.00 0.00 5
2 0.00 0.00 0.00 10
3 0.00 0.00 0.00 8
4 0.00 0.00 0.00 9
5 0.00 0.00 0.00 7
6 0.15 1.00 0.27 6
7 1.00 1.00 1.00 8
8 0.27 1.00 0.43 3
9 0.00 0.00 0.00 2
accuracy 0.36 64
macro avg 0.24 0.40 0.27 64
weighted avg 0.25 0.36 0.26 64
Confusion Matrix:
Hidden state size = 10¶
In [ ]:
INPUT_DIM = 10
HIDDEN_DIM = 10
OUTPUT_DIM = 10
NUM_LAYERS = 5
LR = 0.001
K = 64
pos = 1
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [ ]:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [ ]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for iteration in range(10*1000):
L = random.randint(3, 10)
_, inputs, labels = get_batch(K, L, pos = pos)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
_, inputs, labels = get_batch(K, L, pos = pos)
# Forward pass only to get logits/output
outputs = model(inputs)
val_cost = criterion(outputs, labels)
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(iteration, loss.item(), accuracy))
print('Finished Training')
Streaming output truncated to the last 5000 lines.
Iteration: 5001. Train Loss: 0.08089224249124527. Validation Accuracy: 100.0
Iteration: 5002. Train Loss: 0.10918404161930084. Validation Accuracy: 100.0
Iteration: 5003. Train Loss: 0.21055129170417786. Validation Accuracy: 100.0
Iteration: 5004. Train Loss: 0.10063257068395615. Validation Accuracy: 100.0
Iteration: 5005. Train Loss: 0.10160911083221436. Validation Accuracy: 100.0
Iteration: 5006. Train Loss: 0.10075515508651733. Validation Accuracy: 100.0
Iteration: 5007. Train Loss: 0.09208465367555618. Validation Accuracy: 100.0
Iteration: 5008. Train Loss: 0.0933263748884201. Validation Accuracy: 100.0
Iteration: 5009. Train Loss: 0.08884292095899582. Validation Accuracy: 100.0
Iteration: 5010. Train Loss: 0.09730369597673416. Validation Accuracy: 100.0
Iteration: 5011. Train Loss: 0.22533993422985077. Validation Accuracy: 100.0
Iteration: 5012. Train Loss: 0.12152764201164246. Validation Accuracy: 100.0
Iteration: 5013. Train Loss: 0.22718925774097443. Validation Accuracy: 100.0
Iteration: 5014. Train Loss: 0.07581770420074463. Validation Accuracy: 100.0
Iteration: 5015. Train Loss: 0.0940837562084198. Validation Accuracy: 100.0
Iteration: 5016. Train Loss: 0.08281942456960678. Validation Accuracy: 100.0
Iteration: 5017. Train Loss: 0.0895228311419487. Validation Accuracy: 100.0
Iteration: 5018. Train Loss: 0.08780651539564133. Validation Accuracy: 100.0
Iteration: 5019. Train Loss: 0.10993445664644241. Validation Accuracy: 100.0
Iteration: 5020. Train Loss: 0.08054196089506149. Validation Accuracy: 100.0
Iteration: 5021. Train Loss: 0.20200730860233307. Validation Accuracy: 100.0
Iteration: 5022. Train Loss: 0.22516293823719025. Validation Accuracy: 100.0
Iteration: 5023. Train Loss: 0.0948152020573616. Validation Accuracy: 100.0
Iteration: 5024. Train Loss: 0.09927338361740112. Validation Accuracy: 100.0
Iteration: 5025. Train Loss: 0.0746697261929512. Validation Accuracy: 100.0
Iteration: 5026. Train Loss: 0.08395581692457199. Validation Accuracy: 100.0
Iteration: 5027. Train Loss: 0.22075001895427704. Validation Accuracy: 100.0
Iteration: 5028. Train Loss: 0.10922381281852722. Validation Accuracy: 100.0
Iteration: 5029. Train Loss: 0.1931721717119217. Validation Accuracy: 100.0
Iteration: 5030. Train Loss: 0.18618372082710266. Validation Accuracy: 100.0
Iteration: 5031. Train Loss: 0.09044796973466873. Validation Accuracy: 100.0
Iteration: 5032. Train Loss: 0.0952494740486145. Validation Accuracy: 100.0
Iteration: 5033. Train Loss: 0.10083836317062378. Validation Accuracy: 100.0
Iteration: 5034. Train Loss: 0.10925008356571198. Validation Accuracy: 100.0
Iteration: 5035. Train Loss: 0.09249920397996902. Validation Accuracy: 100.0
Iteration: 5036. Train Loss: 0.09902067482471466. Validation Accuracy: 100.0
Iteration: 5037. Train Loss: 0.09305024147033691. Validation Accuracy: 100.0
Iteration: 5038. Train Loss: 0.08305776119232178. Validation Accuracy: 100.0
Iteration: 5039. Train Loss: 0.239579439163208. Validation Accuracy: 100.0
Iteration: 5040. Train Loss: 0.08475247770547867. Validation Accuracy: 100.0
Iteration: 5041. Train Loss: 0.08345860987901688. Validation Accuracy: 100.0
Iteration: 5042. Train Loss: 0.0705621987581253. Validation Accuracy: 100.0
Iteration: 5043. Train Loss: 0.08292271941900253. Validation Accuracy: 100.0
Iteration: 5044. Train Loss: 0.09114522486925125. Validation Accuracy: 100.0
Iteration: 5045. Train Loss: 0.10123858600854874. Validation Accuracy: 100.0
Iteration: 5046. Train Loss: 0.07873187214136124. Validation Accuracy: 100.0
Iteration: 5047. Train Loss: 0.21125373244285583. Validation Accuracy: 100.0
Iteration: 5048. Train Loss: 0.08712675422430038. Validation Accuracy: 100.0
Iteration: 5049. Train Loss: 0.07072264701128006. Validation Accuracy: 100.0
Iteration: 5050. Train Loss: 0.07482825219631195. Validation Accuracy: 100.0
Iteration: 5051. Train Loss: 0.10511652380228043. Validation Accuracy: 100.0
Iteration: 5052. Train Loss: 0.08349567651748657. Validation Accuracy: 100.0
Iteration: 5053. Train Loss: 0.09705258905887604. Validation Accuracy: 100.0
Iteration: 5054. Train Loss: 0.08583232015371323. Validation Accuracy: 100.0
Iteration: 5055. Train Loss: 0.19982285797595978. Validation Accuracy: 100.0
Iteration: 5056. Train Loss: 0.08759131282567978. Validation Accuracy: 100.0
Iteration: 5057. Train Loss: 0.07386638224124908. Validation Accuracy: 100.0
Iteration: 5058. Train Loss: 0.07579509913921356. Validation Accuracy: 100.0
Iteration: 5059. Train Loss: 0.0817486047744751. Validation Accuracy: 100.0
Iteration: 5060. Train Loss: 0.10312166810035706. Validation Accuracy: 100.0
Iteration: 5061. Train Loss: 0.09859894961118698. Validation Accuracy: 100.0
Iteration: 5062. Train Loss: 0.08041604608297348. Validation Accuracy: 100.0
Iteration: 5063. Train Loss: 0.08115459978580475. Validation Accuracy: 100.0
Iteration: 5064. Train Loss: 0.08570557087659836. Validation Accuracy: 100.0
Iteration: 5065. Train Loss: 0.0810551866889. Validation Accuracy: 100.0
Iteration: 5066. Train Loss: 0.1030815914273262. Validation Accuracy: 100.0
Iteration: 5067. Train Loss: 0.07681645452976227. Validation Accuracy: 100.0
Iteration: 5068. Train Loss: 0.22191599011421204. Validation Accuracy: 100.0
Iteration: 5069. Train Loss: 0.07332655042409897. Validation Accuracy: 100.0
Iteration: 5070. Train Loss: 0.08461903035640717. Validation Accuracy: 100.0
Iteration: 5071. Train Loss: 0.080842524766922. Validation Accuracy: 100.0
Iteration: 5072. Train Loss: 0.09444156289100647. Validation Accuracy: 100.0
Iteration: 5073. Train Loss: 0.07637033611536026. Validation Accuracy: 100.0
Iteration: 5074. Train Loss: 0.09781050682067871. Validation Accuracy: 100.0
Iteration: 5075. Train Loss: 0.23309879004955292. Validation Accuracy: 100.0
Iteration: 5076. Train Loss: 0.07798636704683304. Validation Accuracy: 100.0
Iteration: 5077. Train Loss: 0.22106443345546722. Validation Accuracy: 100.0
Iteration: 5078. Train Loss: 0.0942361056804657. Validation Accuracy: 100.0
Iteration: 5079. Train Loss: 0.06563294678926468. Validation Accuracy: 100.0
Iteration: 5080. Train Loss: 0.0779077559709549. Validation Accuracy: 100.0
Iteration: 5081. Train Loss: 0.18699243664741516. Validation Accuracy: 100.0
Iteration: 5082. Train Loss: 0.08808443695306778. Validation Accuracy: 100.0
Iteration: 5083. Train Loss: 0.07473178952932358. Validation Accuracy: 100.0
Iteration: 5084. Train Loss: 0.08694057911634445. Validation Accuracy: 100.0
Iteration: 5085. Train Loss: 0.08606819808483124. Validation Accuracy: 100.0
Iteration: 5086. Train Loss: 0.1796342432498932. Validation Accuracy: 100.0
Iteration: 5087. Train Loss: 0.1781228482723236. Validation Accuracy: 100.0
Iteration: 5088. Train Loss: 0.07892633229494095. Validation Accuracy: 100.0
Iteration: 5089. Train Loss: 0.07766609638929367. Validation Accuracy: 100.0
Iteration: 5090. Train Loss: 0.0812152549624443. Validation Accuracy: 100.0
Iteration: 5091. Train Loss: 0.08807048201560974. Validation Accuracy: 100.0
Iteration: 5092. Train Loss: 0.09470529109239578. Validation Accuracy: 100.0
Iteration: 5093. Train Loss: 0.07949968427419662. Validation Accuracy: 100.0
Iteration: 5094. Train Loss: 0.18652652204036713. Validation Accuracy: 100.0
Iteration: 5095. Train Loss: 0.07828151434659958. Validation Accuracy: 100.0
Iteration: 5096. Train Loss: 0.0953507274389267. Validation Accuracy: 100.0
Iteration: 5097. Train Loss: 0.07443546503782272. Validation Accuracy: 100.0
Iteration: 5098. Train Loss: 0.09873217344284058. Validation Accuracy: 100.0
Iteration: 5099. Train Loss: 0.1022079810500145. Validation Accuracy: 100.0
Iteration: 5100. Train Loss: 0.0786547064781189. Validation Accuracy: 100.0
Iteration: 5101. Train Loss: 0.08621565997600555. Validation Accuracy: 100.0
Iteration: 5102. Train Loss: 0.17452161014080048. Validation Accuracy: 100.0
Iteration: 5103. Train Loss: 0.08245667815208435. Validation Accuracy: 100.0
Iteration: 5104. Train Loss: 0.08168651163578033. Validation Accuracy: 100.0
Iteration: 5105. Train Loss: 0.08590373396873474. Validation Accuracy: 100.0
Iteration: 5106. Train Loss: 0.10573574155569077. Validation Accuracy: 100.0
Iteration: 5107. Train Loss: 0.07544533163309097. Validation Accuracy: 100.0
Iteration: 5108. Train Loss: 0.06867862492799759. Validation Accuracy: 100.0
Iteration: 5109. Train Loss: 0.08573692291975021. Validation Accuracy: 100.0
Iteration: 5110. Train Loss: 0.07558707147836685. Validation Accuracy: 100.0
Iteration: 5111. Train Loss: 0.1868107169866562. Validation Accuracy: 100.0
Iteration: 5112. Train Loss: 0.0761931762099266. Validation Accuracy: 100.0
Iteration: 5113. Train Loss: 0.07139550894498825. Validation Accuracy: 100.0
Iteration: 5114. Train Loss: 0.07645054906606674. Validation Accuracy: 100.0
Iteration: 5115. Train Loss: 0.06829451024532318. Validation Accuracy: 100.0
Iteration: 5116. Train Loss: 0.08780622482299805. Validation Accuracy: 100.0
Iteration: 5117. Train Loss: 0.09316657483577728. Validation Accuracy: 100.0
Iteration: 5118. Train Loss: 0.082700215280056. Validation Accuracy: 100.0
Iteration: 5119. Train Loss: 0.07296411693096161. Validation Accuracy: 100.0
Iteration: 5120. Train Loss: 0.0686921626329422. Validation Accuracy: 100.0
Iteration: 5121. Train Loss: 0.0872388631105423. Validation Accuracy: 100.0
Iteration: 5122. Train Loss: 0.07272744178771973. Validation Accuracy: 100.0
Iteration: 5123. Train Loss: 0.17755110561847687. Validation Accuracy: 100.0
Iteration: 5124. Train Loss: 0.09161026775836945. Validation Accuracy: 100.0
Iteration: 5125. Train Loss: 0.07017012685537338. Validation Accuracy: 100.0
Iteration: 5126. Train Loss: 0.06961015611886978. Validation Accuracy: 100.0
Iteration: 5127. Train Loss: 0.07585115730762482. Validation Accuracy: 100.0
Iteration: 5128. Train Loss: 0.08000023663043976. Validation Accuracy: 100.0
Iteration: 5129. Train Loss: 0.0717301070690155. Validation Accuracy: 100.0
Iteration: 5130. Train Loss: 0.07774849236011505. Validation Accuracy: 100.0
Iteration: 5131. Train Loss: 0.09228864312171936. Validation Accuracy: 100.0
Iteration: 5132. Train Loss: 0.06251852959394455. Validation Accuracy: 100.0
Iteration: 5133. Train Loss: 0.0758286789059639. Validation Accuracy: 100.0
Iteration: 5134. Train Loss: 0.08606338500976562. Validation Accuracy: 100.0
Iteration: 5135. Train Loss: 0.08866533637046814. Validation Accuracy: 100.0
Iteration: 5136. Train Loss: 0.08219403773546219. Validation Accuracy: 100.0
Iteration: 5137. Train Loss: 0.08137043565511703. Validation Accuracy: 100.0
Iteration: 5138. Train Loss: 0.09195885807275772. Validation Accuracy: 100.0
Iteration: 5139. Train Loss: 0.07318523526191711. Validation Accuracy: 100.0
Iteration: 5140. Train Loss: 0.08513496816158295. Validation Accuracy: 100.0
Iteration: 5141. Train Loss: 0.09396833926439285. Validation Accuracy: 100.0
Iteration: 5142. Train Loss: 0.07633497565984726. Validation Accuracy: 100.0
Iteration: 5143. Train Loss: 0.08214839547872543. Validation Accuracy: 100.0
Iteration: 5144. Train Loss: 0.0636381134390831. Validation Accuracy: 100.0
Iteration: 5145. Train Loss: 0.08793261647224426. Validation Accuracy: 100.0
Iteration: 5146. Train Loss: 0.08462897688150406. Validation Accuracy: 100.0
Iteration: 5147. Train Loss: 0.07993954420089722. Validation Accuracy: 100.0
Iteration: 5148. Train Loss: 0.07464955747127533. Validation Accuracy: 100.0
Iteration: 5149. Train Loss: 0.073719322681427. Validation Accuracy: 100.0
Iteration: 5150. Train Loss: 0.0757988691329956. Validation Accuracy: 100.0
Iteration: 5151. Train Loss: 0.08894442766904831. Validation Accuracy: 100.0
Iteration: 5152. Train Loss: 0.08233257383108139. Validation Accuracy: 100.0
Iteration: 5153. Train Loss: 0.06958266347646713. Validation Accuracy: 100.0
Iteration: 5154. Train Loss: 0.17160360515117645. Validation Accuracy: 100.0
Iteration: 5155. Train Loss: 0.08081435412168503. Validation Accuracy: 100.0
Iteration: 5156. Train Loss: 0.07472601532936096. Validation Accuracy: 100.0
Iteration: 5157. Train Loss: 0.07641790807247162. Validation Accuracy: 100.0
Iteration: 5158. Train Loss: 0.0619313046336174. Validation Accuracy: 100.0
Iteration: 5159. Train Loss: 0.07444362342357635. Validation Accuracy: 100.0
Iteration: 5160. Train Loss: 0.05798220634460449. Validation Accuracy: 100.0
Iteration: 5161. Train Loss: 0.07414843142032623. Validation Accuracy: 100.0
Iteration: 5162. Train Loss: 0.08348872512578964. Validation Accuracy: 100.0
Iteration: 5163. Train Loss: 0.09026115387678146. Validation Accuracy: 100.0
Iteration: 5164. Train Loss: 0.07879512012004852. Validation Accuracy: 100.0
Iteration: 5165. Train Loss: 0.0678771436214447. Validation Accuracy: 100.0
Iteration: 5166. Train Loss: 0.06099078431725502. Validation Accuracy: 100.0
Iteration: 5167. Train Loss: 0.0693046972155571. Validation Accuracy: 100.0
Iteration: 5168. Train Loss: 0.17387977242469788. Validation Accuracy: 100.0
Iteration: 5169. Train Loss: 0.191139355301857. Validation Accuracy: 100.0
Iteration: 5170. Train Loss: 0.074017733335495. Validation Accuracy: 100.0
Iteration: 5171. Train Loss: 0.06989852339029312. Validation Accuracy: 100.0
Iteration: 5172. Train Loss: 0.06515707820653915. Validation Accuracy: 100.0
Iteration: 5173. Train Loss: 0.07608398050069809. Validation Accuracy: 100.0
Iteration: 5174. Train Loss: 0.07095514982938766. Validation Accuracy: 100.0
Iteration: 5175. Train Loss: 0.06409025937318802. Validation Accuracy: 100.0
Iteration: 5176. Train Loss: 0.06472358107566833. Validation Accuracy: 100.0
Iteration: 5177. Train Loss: 0.06881463527679443. Validation Accuracy: 100.0
Iteration: 5178. Train Loss: 0.07495585829019547. Validation Accuracy: 100.0
Iteration: 5179. Train Loss: 0.07558809220790863. Validation Accuracy: 100.0
Iteration: 5180. Train Loss: 0.08223569393157959. Validation Accuracy: 100.0
Iteration: 5181. Train Loss: 0.07028011977672577. Validation Accuracy: 98.4375
Iteration: 5182. Train Loss: 0.0846264511346817. Validation Accuracy: 100.0
Iteration: 5183. Train Loss: 0.07099892944097519. Validation Accuracy: 100.0
Iteration: 5184. Train Loss: 0.06535010039806366. Validation Accuracy: 100.0
Iteration: 5185. Train Loss: 0.07966002821922302. Validation Accuracy: 100.0
Iteration: 5186. Train Loss: 0.08651717007160187. Validation Accuracy: 100.0
Iteration: 5187. Train Loss: 0.06914090365171432. Validation Accuracy: 100.0
Iteration: 5188. Train Loss: 0.0709163248538971. Validation Accuracy: 100.0
Iteration: 5189. Train Loss: 0.07131107151508331. Validation Accuracy: 100.0
Iteration: 5190. Train Loss: 0.07774407416582108. Validation Accuracy: 100.0
Iteration: 5191. Train Loss: 0.17922040820121765. Validation Accuracy: 100.0
Iteration: 5192. Train Loss: 0.06623056530952454. Validation Accuracy: 100.0
Iteration: 5193. Train Loss: 0.07470904290676117. Validation Accuracy: 100.0
Iteration: 5194. Train Loss: 0.07099679857492447. Validation Accuracy: 100.0
Iteration: 5195. Train Loss: 0.056749146431684494. Validation Accuracy: 100.0
Iteration: 5196. Train Loss: 0.067862868309021. Validation Accuracy: 100.0
Iteration: 5197. Train Loss: 0.19195961952209473. Validation Accuracy: 100.0
Iteration: 5198. Train Loss: 0.06092345714569092. Validation Accuracy: 100.0
Iteration: 5199. Train Loss: 0.07068629562854767. Validation Accuracy: 100.0
Iteration: 5200. Train Loss: 0.07428181916475296. Validation Accuracy: 100.0
Iteration: 5201. Train Loss: 0.07136426866054535. Validation Accuracy: 100.0
Iteration: 5202. Train Loss: 0.06187323108315468. Validation Accuracy: 100.0
Iteration: 5203. Train Loss: 0.06209201738238335. Validation Accuracy: 100.0
Iteration: 5204. Train Loss: 0.07013196498155594. Validation Accuracy: 100.0
Iteration: 5205. Train Loss: 0.18095724284648895. Validation Accuracy: 100.0
Iteration: 5206. Train Loss: 0.06838001310825348. Validation Accuracy: 100.0
Iteration: 5207. Train Loss: 0.0828806608915329. Validation Accuracy: 100.0
Iteration: 5208. Train Loss: 0.08017582446336746. Validation Accuracy: 100.0
Iteration: 5209. Train Loss: 0.06679581850767136. Validation Accuracy: 100.0
Iteration: 5210. Train Loss: 0.17757810652256012. Validation Accuracy: 100.0
Iteration: 5211. Train Loss: 0.061282895505428314. Validation Accuracy: 100.0
Iteration: 5212. Train Loss: 0.07483997195959091. Validation Accuracy: 100.0
Iteration: 5213. Train Loss: 0.0606473907828331. Validation Accuracy: 100.0
Iteration: 5214. Train Loss: 0.055904991924762726. Validation Accuracy: 100.0
Iteration: 5215. Train Loss: 0.05942782759666443. Validation Accuracy: 100.0
Iteration: 5216. Train Loss: 0.07141444087028503. Validation Accuracy: 100.0
Iteration: 5217. Train Loss: 0.07965274155139923. Validation Accuracy: 100.0
Iteration: 5218. Train Loss: 0.08894646912813187. Validation Accuracy: 100.0
Iteration: 5219. Train Loss: 0.07415368407964706. Validation Accuracy: 100.0
Iteration: 5220. Train Loss: 0.0747075304389. Validation Accuracy: 100.0
Iteration: 5221. Train Loss: 0.06495746225118637. Validation Accuracy: 100.0
Iteration: 5222. Train Loss: 0.17039938271045685. Validation Accuracy: 100.0
Iteration: 5223. Train Loss: 0.190784752368927. Validation Accuracy: 100.0
Iteration: 5224. Train Loss: 0.17864252626895905. Validation Accuracy: 100.0
Iteration: 5225. Train Loss: 0.0622892752289772. Validation Accuracy: 100.0
Iteration: 5226. Train Loss: 0.07466573268175125. Validation Accuracy: 100.0
Iteration: 5227. Train Loss: 0.15746383368968964. Validation Accuracy: 100.0
Iteration: 5228. Train Loss: 0.07786834239959717. Validation Accuracy: 100.0
Iteration: 5229. Train Loss: 0.06809951364994049. Validation Accuracy: 100.0
Iteration: 5230. Train Loss: 0.17545713484287262. Validation Accuracy: 100.0
Iteration: 5231. Train Loss: 0.2104577124118805. Validation Accuracy: 100.0
Iteration: 5232. Train Loss: 0.049419403076171875. Validation Accuracy: 100.0
Iteration: 5233. Train Loss: 0.07334503531455994. Validation Accuracy: 100.0
Iteration: 5234. Train Loss: 0.07649114727973938. Validation Accuracy: 100.0
Iteration: 5235. Train Loss: 0.0737965926527977. Validation Accuracy: 100.0
Iteration: 5236. Train Loss: 0.06146771460771561. Validation Accuracy: 100.0
Iteration: 5237. Train Loss: 0.1621924340724945. Validation Accuracy: 100.0
Iteration: 5238. Train Loss: 0.07590100914239883. Validation Accuracy: 100.0
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Iteration: 9814. Train Loss: 0.007252303417772055. Validation Accuracy: 100.0
Iteration: 9815. Train Loss: 0.007040209136903286. Validation Accuracy: 100.0
Iteration: 9816. Train Loss: 0.0068880412727594376. Validation Accuracy: 100.0
Iteration: 9817. Train Loss: 0.007263944949954748. Validation Accuracy: 100.0
Iteration: 9818. Train Loss: 0.007244711741805077. Validation Accuracy: 100.0
Iteration: 9819. Train Loss: 0.008073387667536736. Validation Accuracy: 100.0
Iteration: 9820. Train Loss: 0.007384769152849913. Validation Accuracy: 100.0
Iteration: 9821. Train Loss: 0.0067936815321445465. Validation Accuracy: 100.0
Iteration: 9822. Train Loss: 0.0068088253028690815. Validation Accuracy: 100.0
Iteration: 9823. Train Loss: 0.007098664063960314. Validation Accuracy: 100.0
Iteration: 9824. Train Loss: 0.006562831811606884. Validation Accuracy: 100.0
Iteration: 9825. Train Loss: 0.021248340606689453. Validation Accuracy: 100.0
Iteration: 9826. Train Loss: 0.006368651986122131. Validation Accuracy: 100.0
Iteration: 9827. Train Loss: 0.0069597456604242325. Validation Accuracy: 100.0
Iteration: 9828. Train Loss: 0.007741635199636221. Validation Accuracy: 100.0
Iteration: 9829. Train Loss: 0.007215994410216808. Validation Accuracy: 100.0
Iteration: 9830. Train Loss: 0.01903514564037323. Validation Accuracy: 100.0
Iteration: 9831. Train Loss: 0.006918004713952541. Validation Accuracy: 100.0
Iteration: 9832. Train Loss: 0.018845515325665474. Validation Accuracy: 100.0
Iteration: 9833. Train Loss: 0.007373996544629335. Validation Accuracy: 100.0
Iteration: 9834. Train Loss: 0.007555163465440273. Validation Accuracy: 100.0
Iteration: 9835. Train Loss: 0.007790528703480959. Validation Accuracy: 100.0
Iteration: 9836. Train Loss: 0.021573543548583984. Validation Accuracy: 100.0
Iteration: 9837. Train Loss: 0.020685549825429916. Validation Accuracy: 100.0
Iteration: 9838. Train Loss: 0.007243994623422623. Validation Accuracy: 100.0
Iteration: 9839. Train Loss: 0.007193553261458874. Validation Accuracy: 100.0
Iteration: 9840. Train Loss: 0.006639705505222082. Validation Accuracy: 100.0
Iteration: 9841. Train Loss: 0.006450377870351076. Validation Accuracy: 100.0
Iteration: 9842. Train Loss: 0.008413400501012802. Validation Accuracy: 100.0
Iteration: 9843. Train Loss: 0.007436409126967192. Validation Accuracy: 100.0
Iteration: 9844. Train Loss: 0.006891956087201834. Validation Accuracy: 100.0
Iteration: 9845. Train Loss: 0.006889010313898325. Validation Accuracy: 100.0
Iteration: 9846. Train Loss: 0.007411617320030928. Validation Accuracy: 100.0
Iteration: 9847. Train Loss: 0.006669999100267887. Validation Accuracy: 100.0
Iteration: 9848. Train Loss: 0.007493788376450539. Validation Accuracy: 100.0
Iteration: 9849. Train Loss: 0.006982565391808748. Validation Accuracy: 100.0
Iteration: 9850. Train Loss: 0.00667453883215785. Validation Accuracy: 100.0
Iteration: 9851. Train Loss: 0.0066656675189733505. Validation Accuracy: 100.0
Iteration: 9852. Train Loss: 0.006757955066859722. Validation Accuracy: 100.0
Iteration: 9853. Train Loss: 0.020288076251745224. Validation Accuracy: 100.0
Iteration: 9854. Train Loss: 0.006567297037690878. Validation Accuracy: 100.0
Iteration: 9855. Train Loss: 0.022753994911909103. Validation Accuracy: 100.0
Iteration: 9856. Train Loss: 0.00824675802141428. Validation Accuracy: 100.0
Iteration: 9857. Train Loss: 0.006862238049507141. Validation Accuracy: 100.0
Iteration: 9858. Train Loss: 0.006517271511256695. Validation Accuracy: 100.0
Iteration: 9859. Train Loss: 0.021269865334033966. Validation Accuracy: 100.0
Iteration: 9860. Train Loss: 0.006721826735883951. Validation Accuracy: 100.0
Iteration: 9861. Train Loss: 0.006463909987360239. Validation Accuracy: 100.0
Iteration: 9862. Train Loss: 0.02032935991883278. Validation Accuracy: 100.0
Iteration: 9863. Train Loss: 0.019445886835455894. Validation Accuracy: 100.0
Iteration: 9864. Train Loss: 0.007127923890948296. Validation Accuracy: 100.0
Iteration: 9865. Train Loss: 0.006513889878988266. Validation Accuracy: 100.0
Iteration: 9866. Train Loss: 0.006724484730511904. Validation Accuracy: 100.0
Iteration: 9867. Train Loss: 0.006647093687206507. Validation Accuracy: 100.0
Iteration: 9868. Train Loss: 0.006825926713645458. Validation Accuracy: 100.0
Iteration: 9869. Train Loss: 0.006785209756344557. Validation Accuracy: 100.0
Iteration: 9870. Train Loss: 0.0069136954843997955. Validation Accuracy: 100.0
Iteration: 9871. Train Loss: 0.006900622043758631. Validation Accuracy: 100.0
Iteration: 9872. Train Loss: 0.006987841334193945. Validation Accuracy: 100.0
Iteration: 9873. Train Loss: 0.0065477872267365456. Validation Accuracy: 100.0
Iteration: 9874. Train Loss: 0.021330589428544044. Validation Accuracy: 100.0
Iteration: 9875. Train Loss: 0.021967347711324692. Validation Accuracy: 100.0
Iteration: 9876. Train Loss: 0.007282450795173645. Validation Accuracy: 100.0
Iteration: 9877. Train Loss: 0.007775713223963976. Validation Accuracy: 100.0
Iteration: 9878. Train Loss: 0.00660013547167182. Validation Accuracy: 100.0
Iteration: 9879. Train Loss: 0.006783776450902224. Validation Accuracy: 100.0
Iteration: 9880. Train Loss: 0.006114899646490812. Validation Accuracy: 100.0
Iteration: 9881. Train Loss: 0.006774516310542822. Validation Accuracy: 100.0
Iteration: 9882. Train Loss: 0.006726454012095928. Validation Accuracy: 100.0
Iteration: 9883. Train Loss: 0.007873172871768475. Validation Accuracy: 100.0
Iteration: 9884. Train Loss: 0.01921209506690502. Validation Accuracy: 100.0
Iteration: 9885. Train Loss: 0.006291231140494347. Validation Accuracy: 100.0
Iteration: 9886. Train Loss: 0.006402995437383652. Validation Accuracy: 100.0
Iteration: 9887. Train Loss: 0.006842263042926788. Validation Accuracy: 100.0
Iteration: 9888. Train Loss: 0.006659021135419607. Validation Accuracy: 100.0
Iteration: 9889. Train Loss: 0.0072399708442389965. Validation Accuracy: 100.0
Iteration: 9890. Train Loss: 0.0072192540392279625. Validation Accuracy: 100.0
Iteration: 9891. Train Loss: 0.006999534089118242. Validation Accuracy: 100.0
Iteration: 9892. Train Loss: 0.006869076285511255. Validation Accuracy: 100.0
Iteration: 9893. Train Loss: 0.006306833121925592. Validation Accuracy: 100.0
Iteration: 9894. Train Loss: 0.006790931802242994. Validation Accuracy: 100.0
Iteration: 9895. Train Loss: 0.006703384220600128. Validation Accuracy: 100.0
Iteration: 9896. Train Loss: 0.006098999176174402. Validation Accuracy: 100.0
Iteration: 9897. Train Loss: 0.021881103515625. Validation Accuracy: 100.0
Iteration: 9898. Train Loss: 0.020611578598618507. Validation Accuracy: 100.0
Iteration: 9899. Train Loss: 0.007209856063127518. Validation Accuracy: 100.0
Iteration: 9900. Train Loss: 0.006706012412905693. Validation Accuracy: 100.0
Iteration: 9901. Train Loss: 0.0069023557007312775. Validation Accuracy: 100.0
Iteration: 9902. Train Loss: 0.007136428263038397. Validation Accuracy: 100.0
Iteration: 9903. Train Loss: 0.006958466023206711. Validation Accuracy: 100.0
Iteration: 9904. Train Loss: 0.007335002068430185. Validation Accuracy: 100.0
Iteration: 9905. Train Loss: 0.007143678143620491. Validation Accuracy: 100.0
Iteration: 9906. Train Loss: 0.007078220136463642. Validation Accuracy: 100.0
Iteration: 9907. Train Loss: 0.007251230999827385. Validation Accuracy: 100.0
Iteration: 9908. Train Loss: 0.006865716073662043. Validation Accuracy: 100.0
Iteration: 9909. Train Loss: 0.02127925492823124. Validation Accuracy: 100.0
Iteration: 9910. Train Loss: 0.0070634461008012295. Validation Accuracy: 100.0
Iteration: 9911. Train Loss: 0.006621198263019323. Validation Accuracy: 100.0
Iteration: 9912. Train Loss: 0.006464123260229826. Validation Accuracy: 100.0
Iteration: 9913. Train Loss: 0.007115750573575497. Validation Accuracy: 100.0
Iteration: 9914. Train Loss: 0.006790206767618656. Validation Accuracy: 100.0
Iteration: 9915. Train Loss: 0.006716073025017977. Validation Accuracy: 100.0
Iteration: 9916. Train Loss: 0.006714364513754845. Validation Accuracy: 100.0
Iteration: 9917. Train Loss: 0.007540534250438213. Validation Accuracy: 100.0
Iteration: 9918. Train Loss: 0.00710554514080286. Validation Accuracy: 100.0
Iteration: 9919. Train Loss: 0.020674970000982285. Validation Accuracy: 100.0
Iteration: 9920. Train Loss: 0.0069543407298624516. Validation Accuracy: 100.0
Iteration: 9921. Train Loss: 0.007160790264606476. Validation Accuracy: 100.0
Iteration: 9922. Train Loss: 0.006293590646237135. Validation Accuracy: 100.0
Iteration: 9923. Train Loss: 0.00734591344371438. Validation Accuracy: 100.0
Iteration: 9924. Train Loss: 0.007229630835354328. Validation Accuracy: 100.0
Iteration: 9925. Train Loss: 0.007399064023047686. Validation Accuracy: 100.0
Iteration: 9926. Train Loss: 0.006617066916078329. Validation Accuracy: 100.0
Iteration: 9927. Train Loss: 0.006479735020548105. Validation Accuracy: 100.0
Iteration: 9928. Train Loss: 0.00633956678211689. Validation Accuracy: 100.0
Iteration: 9929. Train Loss: 0.006829815451055765. Validation Accuracy: 100.0
Iteration: 9930. Train Loss: 0.0197607409209013. Validation Accuracy: 100.0
Iteration: 9931. Train Loss: 0.006263407878577709. Validation Accuracy: 100.0
Iteration: 9932. Train Loss: 0.006601300556212664. Validation Accuracy: 100.0
Iteration: 9933. Train Loss: 0.006748189218342304. Validation Accuracy: 100.0
Iteration: 9934. Train Loss: 0.006561304908245802. Validation Accuracy: 100.0
Iteration: 9935. Train Loss: 0.017660249024629593. Validation Accuracy: 100.0
Iteration: 9936. Train Loss: 0.005980244837701321. Validation Accuracy: 100.0
Iteration: 9937. Train Loss: 0.00727201858535409. Validation Accuracy: 100.0
Iteration: 9938. Train Loss: 0.007011543493717909. Validation Accuracy: 100.0
Iteration: 9939. Train Loss: 0.0074899932369589806. Validation Accuracy: 100.0
Iteration: 9940. Train Loss: 0.007388323545455933. Validation Accuracy: 100.0
Iteration: 9941. Train Loss: 0.006971763912588358. Validation Accuracy: 100.0
Iteration: 9942. Train Loss: 0.006261365953832865. Validation Accuracy: 100.0
Iteration: 9943. Train Loss: 0.006688783876597881. Validation Accuracy: 100.0
Iteration: 9944. Train Loss: 0.007169582415372133. Validation Accuracy: 100.0
Iteration: 9945. Train Loss: 0.006576216779649258. Validation Accuracy: 100.0
Iteration: 9946. Train Loss: 0.020044958218932152. Validation Accuracy: 100.0
Iteration: 9947. Train Loss: 0.006703705061227083. Validation Accuracy: 100.0
Iteration: 9948. Train Loss: 0.01923375390470028. Validation Accuracy: 100.0
Iteration: 9949. Train Loss: 0.006745418533682823. Validation Accuracy: 100.0
Iteration: 9950. Train Loss: 0.0063928524032235146. Validation Accuracy: 100.0
Iteration: 9951. Train Loss: 0.020295845344662666. Validation Accuracy: 100.0
Iteration: 9952. Train Loss: 0.007034149952232838. Validation Accuracy: 100.0
Iteration: 9953. Train Loss: 0.006423629820346832. Validation Accuracy: 100.0
Iteration: 9954. Train Loss: 0.00616597943007946. Validation Accuracy: 100.0
Iteration: 9955. Train Loss: 0.01996450498700142. Validation Accuracy: 100.0
Iteration: 9956. Train Loss: 0.007260748650878668. Validation Accuracy: 100.0
Iteration: 9957. Train Loss: 0.019779447466135025. Validation Accuracy: 100.0
Iteration: 9958. Train Loss: 0.006216899026185274. Validation Accuracy: 100.0
Iteration: 9959. Train Loss: 0.006788142025470734. Validation Accuracy: 100.0
Iteration: 9960. Train Loss: 0.007260004058480263. Validation Accuracy: 100.0
Iteration: 9961. Train Loss: 0.006644079461693764. Validation Accuracy: 100.0
Iteration: 9962. Train Loss: 0.019489118829369545. Validation Accuracy: 100.0
Iteration: 9963. Train Loss: 0.007654127664864063. Validation Accuracy: 100.0
Iteration: 9964. Train Loss: 0.018158484250307083. Validation Accuracy: 100.0
Iteration: 9965. Train Loss: 0.0063833994790911674. Validation Accuracy: 100.0
Iteration: 9966. Train Loss: 0.006669690366834402. Validation Accuracy: 100.0
Iteration: 9967. Train Loss: 0.007371576502919197. Validation Accuracy: 100.0
Iteration: 9968. Train Loss: 0.006682556588202715. Validation Accuracy: 100.0
Iteration: 9969. Train Loss: 0.0065706088207662106. Validation Accuracy: 100.0
Iteration: 9970. Train Loss: 0.006946538109332323. Validation Accuracy: 100.0
Iteration: 9971. Train Loss: 0.006854574661701918. Validation Accuracy: 100.0
Iteration: 9972. Train Loss: 0.0060468330048024654. Validation Accuracy: 100.0
Iteration: 9973. Train Loss: 0.018924681469798088. Validation Accuracy: 100.0
Iteration: 9974. Train Loss: 0.006714662071317434. Validation Accuracy: 100.0
Iteration: 9975. Train Loss: 0.006439595483243465. Validation Accuracy: 100.0
Iteration: 9976. Train Loss: 0.0067869690246880054. Validation Accuracy: 100.0
Iteration: 9977. Train Loss: 0.0069501674734056. Validation Accuracy: 100.0
Iteration: 9978. Train Loss: 0.0063971565105021. Validation Accuracy: 100.0
Iteration: 9979. Train Loss: 0.019931429997086525. Validation Accuracy: 100.0
Iteration: 9980. Train Loss: 0.0060185897164046764. Validation Accuracy: 100.0
Iteration: 9981. Train Loss: 0.0066426414996385574. Validation Accuracy: 100.0
Iteration: 9982. Train Loss: 0.006286270916461945. Validation Accuracy: 100.0
Iteration: 9983. Train Loss: 0.006155886221677065. Validation Accuracy: 100.0
Iteration: 9984. Train Loss: 0.005970698315650225. Validation Accuracy: 100.0
Iteration: 9985. Train Loss: 0.007079125382006168. Validation Accuracy: 100.0
Iteration: 9986. Train Loss: 0.00664270156994462. Validation Accuracy: 100.0
Iteration: 9987. Train Loss: 0.007033218163996935. Validation Accuracy: 100.0
Iteration: 9988. Train Loss: 0.0065048858523368835. Validation Accuracy: 100.0
Iteration: 9989. Train Loss: 0.0068712299689650536. Validation Accuracy: 100.0
Iteration: 9990. Train Loss: 0.0061008017510175705. Validation Accuracy: 100.0
Iteration: 9991. Train Loss: 0.007314924616366625. Validation Accuracy: 100.0
Iteration: 9992. Train Loss: 0.006500945892184973. Validation Accuracy: 100.0
Iteration: 9993. Train Loss: 0.01670185662806034. Validation Accuracy: 100.0
Iteration: 9994. Train Loss: 0.007023983169347048. Validation Accuracy: 100.0
Iteration: 9995. Train Loss: 0.006849291734397411. Validation Accuracy: 100.0
Iteration: 9996. Train Loss: 0.006445280276238918. Validation Accuracy: 100.0
Iteration: 9997. Train Loss: 0.006741208024322987. Validation Accuracy: 100.0
Iteration: 9998. Train Loss: 0.005980102811008692. Validation Accuracy: 100.0
Iteration: 9999. Train Loss: 0.0069137136451900005. Validation Accuracy: 100.0
Finished Training
Learning curves¶
In [ ]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [ ]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Performance on the Test set¶
In [ ]:
L = random.randint(3, 10)
_, inputs, labels = get_batch(K, L, pos = pos)
# Forward pass only to get logits/output
outputs = model(inputs)
test_cost = criterion(outputs, labels)
print("Cost function value: " + str(test_cost.item()))
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
print("Accuracy: " + str(accuracy_score(labels.numpy(), predicted.numpy())))
print("Classification Report: \n")
print(classification_report(labels.numpy(), predicted.numpy(), zero_division = 0))
print("Confusion Matrix: \n")
cm = confusion_matrix(labels.numpy(), predicted.numpy(), labels = range(10))
disp = ConfusionMatrixDisplay(confusion_matrix = cm,
display_labels = range(10))
disp.plot()
plt.show()
Cost function value: 0.006117354147136211
Accuracy: 1.0
Classification Report:
precision recall f1-score support
0 1.00 1.00 1.00 9
1 1.00 1.00 1.00 5
2 1.00 1.00 1.00 8
3 1.00 1.00 1.00 4
4 1.00 1.00 1.00 6
5 1.00 1.00 1.00 3
6 1.00 1.00 1.00 9
7 1.00 1.00 1.00 8
8 1.00 1.00 1.00 7
9 1.00 1.00 1.00 5
accuracy 1.00 64
macro avg 1.00 1.00 1.00 64
weighted avg 1.00 1.00 1.00 64
Confusion Matrix:
Performance on sample sequences of varying lengths¶
The best model is obtained with a hidden state size of 10
In [ ]:
L = 3
seq, inputs, labels = get_batch(5, L, pos = pos)
outputs = model(inputs)
_, predicted = torch.max(outputs.data, 1)
for i in range(5):
# print('Sequence: ', i + 1)
print('Input sequence: ', seq[i])
print('Target: ', labels[i].item())
print('Predicted value: ', predicted[i].item())
print('\n')
Input sequence: [7, 9, 1] Target: 9 Predicted value: 9 Input sequence: [1, 2, 4] Target: 2 Predicted value: 2 Input sequence: [0, 7, 3] Target: 7 Predicted value: 7 Input sequence: [3, 5, 9] Target: 5 Predicted value: 5 Input sequence: [9, 5, 0] Target: 5 Predicted value: 5
In [ ]:
L = 5
seq, inputs, labels = get_batch(5, L, pos = pos)
outputs = model(inputs)
_, predicted = torch.max(outputs.data, 1)
for i in range(5):
# print('Sequence: ', i + 1)
print('Input sequence: ', seq[i])
print('Target: ', labels[i].item())
print('Predicted value: ', predicted[i].item())
print('\n')
Input sequence: [9, 8, 4, 2, 1] Target: 8 Predicted value: 8 Input sequence: [9, 4, 0, 3, 6] Target: 4 Predicted value: 4 Input sequence: [4, 7, 8, 1, 2] Target: 7 Predicted value: 7 Input sequence: [2, 0, 3, 6, 1] Target: 0 Predicted value: 0 Input sequence: [8, 6, 3, 2, 4] Target: 6 Predicted value: 6
In [ ]:
L = 7
seq, inputs, labels = get_batch(5, L, pos = pos)
outputs = model(inputs)
_, predicted = torch.max(outputs.data, 1)
for i in range(5):
# print('Sequence: ', i + 1)
print('Input sequence: ', seq[i])
print('Target: ', labels[i].item())
print('Predicted value: ', predicted[i].item())
print('\n')
Input sequence: [9, 2, 6, 0, 5, 4, 8] Target: 2 Predicted value: 2 Input sequence: [0, 5, 7, 8, 4, 6, 2] Target: 5 Predicted value: 5 Input sequence: [2, 9, 8, 1, 7, 3, 4] Target: 9 Predicted value: 9 Input sequence: [4, 7, 6, 3, 9, 0, 2] Target: 7 Predicted value: 7 Input sequence: [9, 5, 7, 1, 4, 2, 3] Target: 5 Predicted value: 5
In [ ]:
L = 9
seq, inputs, labels = get_batch(5, L, pos = pos)
outputs = model(inputs)
_, predicted = torch.max(outputs.data, 1)
for i in range(5):
# print('Sequence: ', i + 1)
print('Input sequence: ', seq[i])
print('Target: ', labels[i].item())
print('Predicted value: ', predicted[i].item())
print('\n')
Input sequence: [1, 2, 9, 8, 4, 3, 0, 5, 7] Target: 2 Predicted value: 2 Input sequence: [3, 1, 5, 0, 4, 6, 7, 9, 2] Target: 1 Predicted value: 1 Input sequence: [2, 4, 5, 7, 0, 8, 3, 6, 1] Target: 4 Predicted value: 4 Input sequence: [8, 4, 9, 7, 6, 5, 0, 1, 3] Target: 4 Predicted value: 4 Input sequence: [3, 6, 5, 8, 2, 7, 4, 1, 0] Target: 6 Predicted value: 6
3. Adding two binary strings¶
In [37]:
def get_batch(K, L):
max_num = (2 ** L) - 1
X = np.zeros((K, L + 1, 2))
y = np.zeros((K, L + 1, 1))
for i in range(K):
a, b = random.sample(range(max_num + 1), 2)
c = a + b
a_bin = bin(a)[2:]
b_bin = bin(b)[2:]
c_bin = bin(c)[2:]
a_bin = ('0' * (L + 1 - len(a_bin))) + a_bin
b_bin = ('0' * (L + 1 - len(b_bin))) + b_bin
c_bin = ('0' * (L + 1 - len(c_bin))) + c_bin
for j in range(L, -1, -1):
if a_bin[j] == '1':
X[i, j, 0] += 1
if b_bin[j] == '1':
X[i, j, 1] += 1
if c_bin[j] == '1':
y[i, j, 0] += 1
return torch.tensor(X).float(), torch.tensor(y).long()
In [38]:
class RNN(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, layers = 1):
super().__init__()
self.layers = layers
self.hidden_dim = hidden_dim
self.rnn = nn.LSTM(input_dim, hidden_dim, num_layers = layers, batch_first = True)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, text):
#text = [sent len, batch size]
#embedded = [sent len, batch size, emb dim]
output, (hidden, cell) = self.rnn(text)
#output = [sent len, batch size, hid dim]
#hidden = [1, batch size, hid dim]
# assert torch.equal(output[-1,:,:], hidden.squeeze(0))
# hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)
return self.fc(output)
Hidden state size = 2¶
In [ ]:
INPUT_DIM = 2
HIDDEN_DIM = 2
OUTPUT_DIM = 2
NUM_LAYERS = 5
LR = 0.001
K = 64
L = 5
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [ ]:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [ ]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for iteration in range(5000):
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs.view(-1, 2), labels)
loss.backward()
optimizer.step()
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# Forward pass only to get logits/output
outputs = model(inputs)
val_cost = criterion(outputs.view(-1, 2), labels)
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(iteration, loss.item(), accuracy))
print('Finished Training')
Streaming output truncated to the last 5000 lines.
Iteration: 1. Train Loss: 0.6964406967163086. Validation Accuracy: 46.35416793823242
Iteration: 2. Train Loss: 0.6946040987968445. Validation Accuracy: 53.90625
Iteration: 3. Train Loss: 0.6912806630134583. Validation Accuracy: 46.09375
Iteration: 4. Train Loss: 0.6934769749641418. Validation Accuracy: 51.82291793823242
Iteration: 5. Train Loss: 0.6934767365455627. Validation Accuracy: 50.78125
Iteration: 6. Train Loss: 0.6929764747619629. Validation Accuracy: 53.125
Iteration: 7. Train Loss: 0.6942999362945557. Validation Accuracy: 50.52083206176758
Iteration: 8. Train Loss: 0.6950550079345703. Validation Accuracy: 50.0
Iteration: 9. Train Loss: 0.6957116723060608. Validation Accuracy: 50.52083206176758
Iteration: 10. Train Loss: 0.6941995024681091. Validation Accuracy: 51.30208206176758
Iteration: 11. Train Loss: 0.6918745040893555. Validation Accuracy: 50.52083206176758
Iteration: 12. Train Loss: 0.6939189434051514. Validation Accuracy: 52.86458206176758
Iteration: 13. Train Loss: 0.692415177822113. Validation Accuracy: 52.34375
Iteration: 14. Train Loss: 0.6940564513206482. Validation Accuracy: 48.95833206176758
Iteration: 15. Train Loss: 0.6933107376098633. Validation Accuracy: 52.34375
Iteration: 16. Train Loss: 0.6944863200187683. Validation Accuracy: 50.52083206176758
Iteration: 17. Train Loss: 0.6942107677459717. Validation Accuracy: 46.875
Iteration: 18. Train Loss: 0.6941261291503906. Validation Accuracy: 48.69791793823242
Iteration: 19. Train Loss: 0.6916928887367249. Validation Accuracy: 52.34375
Iteration: 20. Train Loss: 0.6935155391693115. Validation Accuracy: 52.60416793823242
Iteration: 21. Train Loss: 0.6940814852714539. Validation Accuracy: 51.04166793823242
Iteration: 22. Train Loss: 0.6940464377403259. Validation Accuracy: 44.79166793823242
Iteration: 23. Train Loss: 0.693580687046051. Validation Accuracy: 50.0
Iteration: 24. Train Loss: 0.6934645771980286. Validation Accuracy: 50.26041793823242
Iteration: 25. Train Loss: 0.6934095025062561. Validation Accuracy: 49.73958206176758
Iteration: 26. Train Loss: 0.6934189796447754. Validation Accuracy: 48.17708206176758
Iteration: 27. Train Loss: 0.6929464340209961. Validation Accuracy: 50.52083206176758
Iteration: 28. Train Loss: 0.6935132145881653. Validation Accuracy: 52.08333206176758
Iteration: 29. Train Loss: 0.6928703188896179. Validation Accuracy: 46.09375
Iteration: 30. Train Loss: 0.6929253935813904. Validation Accuracy: 48.4375
Iteration: 31. Train Loss: 0.6928066611289978. Validation Accuracy: 44.79166793823242
Iteration: 32. Train Loss: 0.6934276223182678. Validation Accuracy: 50.52083206176758
Iteration: 33. Train Loss: 0.6934442520141602. Validation Accuracy: 51.5625
Iteration: 34. Train Loss: 0.6939263343811035. Validation Accuracy: 49.21875
Iteration: 35. Train Loss: 0.6935169696807861. Validation Accuracy: 48.95833206176758
Iteration: 36. Train Loss: 0.6932253837585449. Validation Accuracy: 49.73958206176758
Iteration: 37. Train Loss: 0.6932324767112732. Validation Accuracy: 52.08333206176758
Iteration: 38. Train Loss: 0.6938610076904297. Validation Accuracy: 53.64583206176758
Iteration: 39. Train Loss: 0.6940128207206726. Validation Accuracy: 52.60416793823242
Iteration: 40. Train Loss: 0.6937869191169739. Validation Accuracy: 44.79166793823242
Iteration: 41. Train Loss: 0.6934776306152344. Validation Accuracy: 47.91666793823242
Iteration: 42. Train Loss: 0.6931406855583191. Validation Accuracy: 51.30208206176758
Iteration: 43. Train Loss: 0.6926760673522949. Validation Accuracy: 48.95833206176758
Iteration: 44. Train Loss: 0.6932728290557861. Validation Accuracy: 51.30208206176758
Iteration: 45. Train Loss: 0.6936354041099548. Validation Accuracy: 53.125
Iteration: 46. Train Loss: 0.6923972964286804. Validation Accuracy: 53.38541793823242
Iteration: 47. Train Loss: 0.6936740875244141. Validation Accuracy: 48.69791793823242
Iteration: 48. Train Loss: 0.6924693584442139. Validation Accuracy: 50.26041793823242
Iteration: 49. Train Loss: 0.6935511231422424. Validation Accuracy: 52.60416793823242
Iteration: 50. Train Loss: 0.6940708160400391. Validation Accuracy: 51.82291793823242
Iteration: 51. Train Loss: 0.693464457988739. Validation Accuracy: 50.52083206176758
Iteration: 52. Train Loss: 0.6930153369903564. Validation Accuracy: 51.30208206176758
Iteration: 53. Train Loss: 0.6923305988311768. Validation Accuracy: 53.90625
Iteration: 54. Train Loss: 0.6930203437805176. Validation Accuracy: 53.90625
Iteration: 55. Train Loss: 0.6929579377174377. Validation Accuracy: 53.38541793823242
Iteration: 56. Train Loss: 0.6936741471290588. Validation Accuracy: 51.30208206176758
Iteration: 57. Train Loss: 0.693361222743988. Validation Accuracy: 53.38541793823242
Iteration: 58. Train Loss: 0.693289577960968. Validation Accuracy: 45.05208206176758
Iteration: 59. Train Loss: 0.6937330365180969. Validation Accuracy: 50.52083206176758
Iteration: 60. Train Loss: 0.6927468180656433. Validation Accuracy: 47.65625
Iteration: 61. Train Loss: 0.6931509375572205. Validation Accuracy: 58.07291793823242
Iteration: 62. Train Loss: 0.6940497756004333. Validation Accuracy: 52.08333206176758
Iteration: 63. Train Loss: 0.6932730674743652. Validation Accuracy: 50.0
Iteration: 64. Train Loss: 0.6922711730003357. Validation Accuracy: 48.17708206176758
Iteration: 65. Train Loss: 0.6934380531311035. Validation Accuracy: 49.21875
Iteration: 66. Train Loss: 0.6927010416984558. Validation Accuracy: 49.73958206176758
Iteration: 67. Train Loss: 0.6931460499763489. Validation Accuracy: 49.21875
Iteration: 68. Train Loss: 0.6927018165588379. Validation Accuracy: 46.61458206176758
Iteration: 69. Train Loss: 0.6934938430786133. Validation Accuracy: 53.125
Iteration: 70. Train Loss: 0.6929168701171875. Validation Accuracy: 53.38541793823242
Iteration: 71. Train Loss: 0.6931440234184265. Validation Accuracy: 51.5625
Iteration: 72. Train Loss: 0.693328320980072. Validation Accuracy: 51.82291793823242
Iteration: 73. Train Loss: 0.6926000714302063. Validation Accuracy: 54.42708206176758
Iteration: 74. Train Loss: 0.6923273205757141. Validation Accuracy: 52.60416793823242
Iteration: 75. Train Loss: 0.6932924389839172. Validation Accuracy: 50.52083206176758
Iteration: 76. Train Loss: 0.6925303936004639. Validation Accuracy: 52.86458206176758
Iteration: 77. Train Loss: 0.6929876208305359. Validation Accuracy: 52.60416793823242
Iteration: 78. Train Loss: 0.6922860145568848. Validation Accuracy: 54.6875
Iteration: 79. Train Loss: 0.692345142364502. Validation Accuracy: 45.05208206176758
Iteration: 80. Train Loss: 0.6933178305625916. Validation Accuracy: 52.08333206176758
Iteration: 81. Train Loss: 0.6923936009407043. Validation Accuracy: 50.78125
Iteration: 82. Train Loss: 0.6920064091682434. Validation Accuracy: 50.78125
Iteration: 83. Train Loss: 0.6931723952293396. Validation Accuracy: 49.47916793823242
Iteration: 84. Train Loss: 0.6935771107673645. Validation Accuracy: 49.21875
Iteration: 85. Train Loss: 0.6926047205924988. Validation Accuracy: 49.21875
Iteration: 86. Train Loss: 0.6932730674743652. Validation Accuracy: 51.04166793823242
Iteration: 87. Train Loss: 0.6930646896362305. Validation Accuracy: 50.52083206176758
Iteration: 88. Train Loss: 0.6936864256858826. Validation Accuracy: 55.72916793823242
Iteration: 89. Train Loss: 0.6926377415657043. Validation Accuracy: 50.26041793823242
Iteration: 90. Train Loss: 0.6931480765342712. Validation Accuracy: 49.47916793823242
Iteration: 91. Train Loss: 0.6933665871620178. Validation Accuracy: 53.125
Iteration: 92. Train Loss: 0.6933503746986389. Validation Accuracy: 50.26041793823242
Iteration: 93. Train Loss: 0.6930160522460938. Validation Accuracy: 51.30208206176758
Iteration: 94. Train Loss: 0.6934520602226257. Validation Accuracy: 47.91666793823242
Iteration: 95. Train Loss: 0.6925800442695618. Validation Accuracy: 48.69791793823242
Iteration: 96. Train Loss: 0.6926129460334778. Validation Accuracy: 50.26041793823242
Iteration: 97. Train Loss: 0.6932530999183655. Validation Accuracy: 46.35416793823242
Iteration: 98. Train Loss: 0.6925721168518066. Validation Accuracy: 47.91666793823242
Iteration: 99. Train Loss: 0.6927370429039001. Validation Accuracy: 48.4375
Iteration: 100. Train Loss: 0.6934340000152588. Validation Accuracy: 53.64583206176758
Iteration: 101. Train Loss: 0.6917848587036133. Validation Accuracy: 47.13541793823242
Iteration: 102. Train Loss: 0.6931011080741882. Validation Accuracy: 55.20833206176758
Iteration: 103. Train Loss: 0.6926193237304688. Validation Accuracy: 50.26041793823242
Iteration: 104. Train Loss: 0.6931089758872986. Validation Accuracy: 46.875
Iteration: 105. Train Loss: 0.6923940181732178. Validation Accuracy: 51.5625
Iteration: 106. Train Loss: 0.6933967471122742. Validation Accuracy: 49.21875
Iteration: 107. Train Loss: 0.6924617290496826. Validation Accuracy: 51.5625
Iteration: 108. Train Loss: 0.6936686038970947. Validation Accuracy: 49.73958206176758
Iteration: 109. Train Loss: 0.6923781037330627. Validation Accuracy: 47.91666793823242
Iteration: 110. Train Loss: 0.6930878162384033. Validation Accuracy: 53.125
Iteration: 111. Train Loss: 0.6924476027488708. Validation Accuracy: 46.35416793823242
Iteration: 112. Train Loss: 0.6931447982788086. Validation Accuracy: 50.78125
Iteration: 113. Train Loss: 0.6929208636283875. Validation Accuracy: 54.16666793823242
Iteration: 114. Train Loss: 0.6930150389671326. Validation Accuracy: 48.17708206176758
Iteration: 115. Train Loss: 0.6929125785827637. Validation Accuracy: 50.26041793823242
Iteration: 116. Train Loss: 0.693047285079956. Validation Accuracy: 48.4375
Iteration: 117. Train Loss: 0.693630039691925. Validation Accuracy: 51.82291793823242
Iteration: 118. Train Loss: 0.6933984160423279. Validation Accuracy: 51.5625
Iteration: 119. Train Loss: 0.6929095387458801. Validation Accuracy: 48.95833206176758
Iteration: 120. Train Loss: 0.6936054229736328. Validation Accuracy: 51.04166793823242
Iteration: 121. Train Loss: 0.6929852366447449. Validation Accuracy: 51.82291793823242
Iteration: 122. Train Loss: 0.6922919154167175. Validation Accuracy: 46.35416793823242
Iteration: 123. Train Loss: 0.6941747069358826. Validation Accuracy: 49.21875
Iteration: 124. Train Loss: 0.6931372284889221. Validation Accuracy: 50.52083206176758
Iteration: 125. Train Loss: 0.6933149695396423. Validation Accuracy: 51.30208206176758
Iteration: 126. Train Loss: 0.6921424865722656. Validation Accuracy: 48.17708206176758
Iteration: 127. Train Loss: 0.6929222941398621. Validation Accuracy: 48.17708206176758
Iteration: 128. Train Loss: 0.692559003829956. Validation Accuracy: 48.17708206176758
Iteration: 129. Train Loss: 0.6934167742729187. Validation Accuracy: 52.08333206176758
Iteration: 130. Train Loss: 0.6933886408805847. Validation Accuracy: 48.17708206176758
Iteration: 131. Train Loss: 0.6928747296333313. Validation Accuracy: 50.78125
Iteration: 132. Train Loss: 0.6929893493652344. Validation Accuracy: 51.82291793823242
Iteration: 133. Train Loss: 0.6932585835456848. Validation Accuracy: 46.61458206176758
Iteration: 134. Train Loss: 0.6930826306343079. Validation Accuracy: 50.26041793823242
Iteration: 135. Train Loss: 0.6936644911766052. Validation Accuracy: 49.47916793823242
Iteration: 136. Train Loss: 0.6931807398796082. Validation Accuracy: 48.17708206176758
Iteration: 137. Train Loss: 0.6931260228157043. Validation Accuracy: 48.4375
Iteration: 138. Train Loss: 0.6934236884117126. Validation Accuracy: 50.52083206176758
Iteration: 139. Train Loss: 0.6930065751075745. Validation Accuracy: 54.16666793823242
Iteration: 140. Train Loss: 0.6937432885169983. Validation Accuracy: 51.5625
Iteration: 141. Train Loss: 0.6939365863800049. Validation Accuracy: 50.26041793823242
Iteration: 142. Train Loss: 0.6935052871704102. Validation Accuracy: 52.34375
Iteration: 143. Train Loss: 0.6932945847511292. Validation Accuracy: 50.78125
Iteration: 144. Train Loss: 0.6932550072669983. Validation Accuracy: 50.0
Iteration: 145. Train Loss: 0.6932578086853027. Validation Accuracy: 54.94791793823242
Iteration: 146. Train Loss: 0.6929171085357666. Validation Accuracy: 53.38541793823242
Iteration: 147. Train Loss: 0.6932072043418884. Validation Accuracy: 48.95833206176758
Iteration: 148. Train Loss: 0.6918947696685791. Validation Accuracy: 49.47916793823242
Iteration: 149. Train Loss: 0.6939358115196228. Validation Accuracy: 50.52083206176758
Iteration: 150. Train Loss: 0.692192018032074. Validation Accuracy: 50.26041793823242
Iteration: 151. Train Loss: 0.6938281655311584. Validation Accuracy: 47.91666793823242
Iteration: 152. Train Loss: 0.6930849552154541. Validation Accuracy: 50.0
Iteration: 153. Train Loss: 0.6928079724311829. Validation Accuracy: 50.78125
Iteration: 154. Train Loss: 0.6927475929260254. Validation Accuracy: 49.21875
Iteration: 155. Train Loss: 0.6932079792022705. Validation Accuracy: 48.4375
Iteration: 156. Train Loss: 0.6922294497489929. Validation Accuracy: 53.125
Iteration: 157. Train Loss: 0.6931342482566833. Validation Accuracy: 48.17708206176758
Iteration: 158. Train Loss: 0.6945242881774902. Validation Accuracy: 49.47916793823242
Iteration: 159. Train Loss: 0.6936705708503723. Validation Accuracy: 48.17708206176758
Iteration: 160. Train Loss: 0.6934012770652771. Validation Accuracy: 49.47916793823242
Iteration: 161. Train Loss: 0.6935049891471863. Validation Accuracy: 50.0
Iteration: 162. Train Loss: 0.6929942965507507. Validation Accuracy: 52.08333206176758
Iteration: 163. Train Loss: 0.6934134364128113. Validation Accuracy: 49.47916793823242
Iteration: 164. Train Loss: 0.6932829022407532. Validation Accuracy: 51.30208206176758
Iteration: 165. Train Loss: 0.692988395690918. Validation Accuracy: 51.5625
Iteration: 166. Train Loss: 0.6926212906837463. Validation Accuracy: 47.91666793823242
Iteration: 167. Train Loss: 0.6930186748504639. Validation Accuracy: 48.95833206176758
Iteration: 168. Train Loss: 0.6944950222969055. Validation Accuracy: 50.78125
Iteration: 169. Train Loss: 0.693125307559967. Validation Accuracy: 48.17708206176758
Iteration: 170. Train Loss: 0.6923236846923828. Validation Accuracy: 52.60416793823242
Iteration: 171. Train Loss: 0.693809449672699. Validation Accuracy: 49.47916793823242
Iteration: 172. Train Loss: 0.6930223107337952. Validation Accuracy: 51.04166793823242
Iteration: 173. Train Loss: 0.6926121115684509. Validation Accuracy: 51.5625
Iteration: 174. Train Loss: 0.6927666664123535. Validation Accuracy: 47.39583206176758
Iteration: 175. Train Loss: 0.6930191516876221. Validation Accuracy: 47.91666793823242
Iteration: 176. Train Loss: 0.6930847764015198. Validation Accuracy: 47.39583206176758
Iteration: 177. Train Loss: 0.6929851174354553. Validation Accuracy: 50.78125
Iteration: 178. Train Loss: 0.6927459239959717. Validation Accuracy: 50.0
Iteration: 179. Train Loss: 0.6932621598243713. Validation Accuracy: 51.04166793823242
Iteration: 180. Train Loss: 0.6926763653755188. Validation Accuracy: 51.30208206176758
Iteration: 181. Train Loss: 0.6926117539405823. Validation Accuracy: 50.0
Iteration: 182. Train Loss: 0.6921969056129456. Validation Accuracy: 49.21875
Iteration: 183. Train Loss: 0.6927841305732727. Validation Accuracy: 56.25
Iteration: 184. Train Loss: 0.6936321258544922. Validation Accuracy: 45.3125
Iteration: 185. Train Loss: 0.6926921010017395. Validation Accuracy: 55.46875
Iteration: 186. Train Loss: 0.6935483813285828. Validation Accuracy: 48.4375
Iteration: 187. Train Loss: 0.692338764667511. Validation Accuracy: 50.52083206176758
Iteration: 188. Train Loss: 0.6933879256248474. Validation Accuracy: 53.125
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Iteration: 190. Train Loss: 0.6929811835289001. Validation Accuracy: 48.17708206176758
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Iteration: 198. Train Loss: 0.6925272941589355. Validation Accuracy: 52.08333206176758
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Iteration: 201. Train Loss: 0.6927480101585388. Validation Accuracy: 49.73958206176758
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Iteration: 206. Train Loss: 0.692972719669342. Validation Accuracy: 47.65625
Iteration: 207. Train Loss: 0.6942365169525146. Validation Accuracy: 45.57291793823242
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Iteration: 217. Train Loss: 0.6923827528953552. Validation Accuracy: 50.0
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Iteration: 260. Train Loss: 0.6930075287818909. Validation Accuracy: 47.39583206176758
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Iteration: 4947. Train Loss: 0.5734657645225525. Validation Accuracy: 69.27083587646484
Iteration: 4948. Train Loss: 0.6118178367614746. Validation Accuracy: 59.89583206176758
Iteration: 4949. Train Loss: 0.5617400407791138. Validation Accuracy: 70.3125
Iteration: 4950. Train Loss: 0.563821017742157. Validation Accuracy: 65.10416412353516
Iteration: 4951. Train Loss: 0.5537589192390442. Validation Accuracy: 69.53125
Iteration: 4952. Train Loss: 0.5418049693107605. Validation Accuracy: 61.97916793823242
Iteration: 4953. Train Loss: 0.5708619952201843. Validation Accuracy: 64.84375
Iteration: 4954. Train Loss: 0.5446504950523376. Validation Accuracy: 61.71875
Iteration: 4955. Train Loss: 0.5490365028381348. Validation Accuracy: 71.09375
Iteration: 4956. Train Loss: 0.5599795579910278. Validation Accuracy: 63.80208206176758
Iteration: 4957. Train Loss: 0.5335600972175598. Validation Accuracy: 66.14583587646484
Iteration: 4958. Train Loss: 0.557752788066864. Validation Accuracy: 63.80208206176758
Iteration: 4959. Train Loss: 0.5386367440223694. Validation Accuracy: 64.58333587646484
Iteration: 4960. Train Loss: 0.5759091973304749. Validation Accuracy: 71.61458587646484
Iteration: 4961. Train Loss: 0.5697271227836609. Validation Accuracy: 64.0625
Iteration: 4962. Train Loss: 0.5479004979133606. Validation Accuracy: 64.32291412353516
Iteration: 4963. Train Loss: 0.5802833437919617. Validation Accuracy: 65.625
Iteration: 4964. Train Loss: 0.533652663230896. Validation Accuracy: 61.71875
Iteration: 4965. Train Loss: 0.5347408056259155. Validation Accuracy: 59.89583206176758
Iteration: 4966. Train Loss: 0.5719494223594666. Validation Accuracy: 69.01041412353516
Iteration: 4967. Train Loss: 0.5881325602531433. Validation Accuracy: 58.85416793823242
Iteration: 4968. Train Loss: 0.5832185745239258. Validation Accuracy: 63.02083206176758
Iteration: 4969. Train Loss: 0.575285792350769. Validation Accuracy: 64.0625
Iteration: 4970. Train Loss: 0.5638536214828491. Validation Accuracy: 66.14583587646484
Iteration: 4971. Train Loss: 0.544765830039978. Validation Accuracy: 60.41666793823242
Iteration: 4972. Train Loss: 0.5632537007331848. Validation Accuracy: 70.57291412353516
Iteration: 4973. Train Loss: 0.5669402480125427. Validation Accuracy: 68.75
Iteration: 4974. Train Loss: 0.5693514347076416. Validation Accuracy: 64.58333587646484
Iteration: 4975. Train Loss: 0.5856813788414001. Validation Accuracy: 69.79166412353516
Iteration: 4976. Train Loss: 0.5723783373832703. Validation Accuracy: 66.14583587646484
Iteration: 4977. Train Loss: 0.5676892399787903. Validation Accuracy: 66.40625
Iteration: 4978. Train Loss: 0.5962873101234436. Validation Accuracy: 66.66666412353516
Iteration: 4979. Train Loss: 0.5428128242492676. Validation Accuracy: 72.91666412353516
Iteration: 4980. Train Loss: 0.5755895376205444. Validation Accuracy: 66.14583587646484
Iteration: 4981. Train Loss: 0.5866044163703918. Validation Accuracy: 67.1875
Iteration: 4982. Train Loss: 0.5735285878181458. Validation Accuracy: 67.44791412353516
Iteration: 4983. Train Loss: 0.5464080572128296. Validation Accuracy: 66.92708587646484
Iteration: 4984. Train Loss: 0.5577780604362488. Validation Accuracy: 71.35416412353516
Iteration: 4985. Train Loss: 0.5589550137519836. Validation Accuracy: 75.78125
Iteration: 4986. Train Loss: 0.5598641037940979. Validation Accuracy: 67.96875
Iteration: 4987. Train Loss: 0.5785463452339172. Validation Accuracy: 64.84375
Iteration: 4988. Train Loss: 0.5698948502540588. Validation Accuracy: 70.3125
Iteration: 4989. Train Loss: 0.5467331409454346. Validation Accuracy: 72.91666412353516
Iteration: 4990. Train Loss: 0.5581002235412598. Validation Accuracy: 70.57291412353516
Iteration: 4991. Train Loss: 0.5809758901596069. Validation Accuracy: 68.48958587646484
Iteration: 4992. Train Loss: 0.6036025881767273. Validation Accuracy: 61.71875
Iteration: 4993. Train Loss: 0.5744500756263733. Validation Accuracy: 67.44791412353516
Iteration: 4994. Train Loss: 0.5696960091590881. Validation Accuracy: 67.70833587646484
Iteration: 4995. Train Loss: 0.5490129590034485. Validation Accuracy: 67.96875
Iteration: 4996. Train Loss: 0.5639147758483887. Validation Accuracy: 64.84375
Iteration: 4997. Train Loss: 0.5790320038795471. Validation Accuracy: 75.26041412353516
Iteration: 4998. Train Loss: 0.5703593492507935. Validation Accuracy: 69.79166412353516
Iteration: 4999. Train Loss: 0.5629796981811523. Validation Accuracy: 71.09375
Finished Training
Learning curves¶
In [ ]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [ ]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Accuracy vs L¶
In [ ]:
acc = []
for L in range(1, 21):
inputs, labels = get_batch(100, L)
outputs = model(inputs)
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
acc.append(accuracy_score(labels.flatten().numpy(), predicted.numpy()))
In [ ]:
# Plot the val accuracy
plt.plot(range(1, 21), acc)
plt.ylabel('Accuracy')
plt.xlabel('Sequence length - L')
plt.title('Accuracy vs L plot')
plt.show()
Hidden state size = 5¶
In [ ]:
INPUT_DIM = 2
HIDDEN_DIM = 5
OUTPUT_DIM = 2
NUM_LAYERS = 5
LR = 0.001
K = 64
L = 5
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [ ]:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [ ]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for iteration in range(5000):
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs.view(-1, 2), labels)
loss.backward()
optimizer.step()
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# Forward pass only to get logits/output
outputs = model(inputs)
val_cost = criterion(outputs.view(-1, 2), labels)
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(iteration, loss.item(), accuracy))
print('Finished Training')
Streaming output truncated to the last 5000 lines.
Iteration: 1. Train Loss: 0.6938777565956116. Validation Accuracy: 48.69791793823242
Iteration: 2. Train Loss: 0.6985463500022888. Validation Accuracy: 51.82291793823242
Iteration: 3. Train Loss: 0.695241391658783. Validation Accuracy: 53.125
Iteration: 4. Train Loss: 0.6992513537406921. Validation Accuracy: 52.86458206176758
Iteration: 5. Train Loss: 0.696704626083374. Validation Accuracy: 48.4375
Iteration: 6. Train Loss: 0.6925461292266846. Validation Accuracy: 48.4375
Iteration: 7. Train Loss: 0.6955514550209045. Validation Accuracy: 48.95833206176758
Iteration: 8. Train Loss: 0.6974673271179199. Validation Accuracy: 49.73958206176758
Iteration: 9. Train Loss: 0.6945679187774658. Validation Accuracy: 51.04166793823242
Iteration: 10. Train Loss: 0.6932434439659119. Validation Accuracy: 48.69791793823242
Iteration: 11. Train Loss: 0.6906557679176331. Validation Accuracy: 51.30208206176758
Iteration: 12. Train Loss: 0.6918232440948486. Validation Accuracy: 47.39583206176758
Iteration: 13. Train Loss: 0.6922841668128967. Validation Accuracy: 47.65625
Iteration: 14. Train Loss: 0.6938081383705139. Validation Accuracy: 52.86458206176758
Iteration: 15. Train Loss: 0.6959540247917175. Validation Accuracy: 46.875
Iteration: 16. Train Loss: 0.6916663646697998. Validation Accuracy: 48.4375
Iteration: 17. Train Loss: 0.6949331164360046. Validation Accuracy: 52.34375
Iteration: 18. Train Loss: 0.6925153136253357. Validation Accuracy: 51.04166793823242
Iteration: 19. Train Loss: 0.6920405030250549. Validation Accuracy: 51.5625
Iteration: 20. Train Loss: 0.6964314579963684. Validation Accuracy: 51.04166793823242
Iteration: 21. Train Loss: 0.6926928162574768. Validation Accuracy: 49.73958206176758
Iteration: 22. Train Loss: 0.6931455731391907. Validation Accuracy: 51.04166793823242
Iteration: 23. Train Loss: 0.6927096247673035. Validation Accuracy: 48.95833206176758
Iteration: 24. Train Loss: 0.6922639012336731. Validation Accuracy: 47.39583206176758
Iteration: 25. Train Loss: 0.6962676048278809. Validation Accuracy: 48.4375
Iteration: 26. Train Loss: 0.695497453212738. Validation Accuracy: 50.26041793823242
Iteration: 27. Train Loss: 0.6930528283119202. Validation Accuracy: 51.04166793823242
Iteration: 28. Train Loss: 0.6958387494087219. Validation Accuracy: 47.91666793823242
Iteration: 29. Train Loss: 0.694087028503418. Validation Accuracy: 47.65625
Iteration: 30. Train Loss: 0.6954598426818848. Validation Accuracy: 49.21875
Iteration: 31. Train Loss: 0.691942036151886. Validation Accuracy: 49.21875
Iteration: 32. Train Loss: 0.692238986492157. Validation Accuracy: 49.47916793823242
Iteration: 33. Train Loss: 0.694105327129364. Validation Accuracy: 51.04166793823242
Iteration: 34. Train Loss: 0.6930928230285645. Validation Accuracy: 50.52083206176758
Iteration: 35. Train Loss: 0.6933048367500305. Validation Accuracy: 46.61458206176758
Iteration: 36. Train Loss: 0.6937652230262756. Validation Accuracy: 47.65625
Iteration: 37. Train Loss: 0.6938251852989197. Validation Accuracy: 51.5625
Iteration: 38. Train Loss: 0.6936028599739075. Validation Accuracy: 53.38541793823242
Iteration: 39. Train Loss: 0.6932108998298645. Validation Accuracy: 51.04166793823242
Iteration: 40. Train Loss: 0.6930825114250183. Validation Accuracy: 51.5625
Iteration: 41. Train Loss: 0.6922116875648499. Validation Accuracy: 49.73958206176758
Iteration: 42. Train Loss: 0.6940984129905701. Validation Accuracy: 49.47916793823242
Iteration: 43. Train Loss: 0.6927599906921387. Validation Accuracy: 49.47916793823242
Iteration: 44. Train Loss: 0.693669319152832. Validation Accuracy: 52.60416793823242
Iteration: 45. Train Loss: 0.6928880214691162. Validation Accuracy: 51.30208206176758
Iteration: 46. Train Loss: 0.6931757926940918. Validation Accuracy: 50.52083206176758
Iteration: 47. Train Loss: 0.6941075921058655. Validation Accuracy: 48.95833206176758
Iteration: 48. Train Loss: 0.6932353973388672. Validation Accuracy: 50.52083206176758
Iteration: 49. Train Loss: 0.6929786801338196. Validation Accuracy: 50.26041793823242
Iteration: 50. Train Loss: 0.6932134628295898. Validation Accuracy: 47.39583206176758
Iteration: 51. Train Loss: 0.6936304569244385. Validation Accuracy: 49.47916793823242
Iteration: 52. Train Loss: 0.6937586665153503. Validation Accuracy: 50.0
Iteration: 53. Train Loss: 0.6925460696220398. Validation Accuracy: 50.26041793823242
Iteration: 54. Train Loss: 0.6936628222465515. Validation Accuracy: 50.52083206176758
Iteration: 55. Train Loss: 0.6930742263793945. Validation Accuracy: 48.17708206176758
Iteration: 56. Train Loss: 0.6934056282043457. Validation Accuracy: 50.0
Iteration: 57. Train Loss: 0.6929598450660706. Validation Accuracy: 51.04166793823242
Iteration: 58. Train Loss: 0.6933882236480713. Validation Accuracy: 52.34375
Iteration: 59. Train Loss: 0.6931031346321106. Validation Accuracy: 48.95833206176758
Iteration: 60. Train Loss: 0.6936576962471008. Validation Accuracy: 50.0
Iteration: 61. Train Loss: 0.6932578682899475. Validation Accuracy: 50.26041793823242
Iteration: 62. Train Loss: 0.692915141582489. Validation Accuracy: 48.17708206176758
Iteration: 63. Train Loss: 0.6934192776679993. Validation Accuracy: 47.13541793823242
Iteration: 64. Train Loss: 0.6931452751159668. Validation Accuracy: 52.08333206176758
Iteration: 65. Train Loss: 0.6936492919921875. Validation Accuracy: 46.61458206176758
Iteration: 66. Train Loss: 0.69340580701828. Validation Accuracy: 50.0
Iteration: 67. Train Loss: 0.6930390000343323. Validation Accuracy: 51.30208206176758
Iteration: 68. Train Loss: 0.6937218308448792. Validation Accuracy: 51.04166793823242
Iteration: 69. Train Loss: 0.6934404969215393. Validation Accuracy: 47.39583206176758
Iteration: 70. Train Loss: 0.6926130652427673. Validation Accuracy: 46.35416793823242
Iteration: 71. Train Loss: 0.693068265914917. Validation Accuracy: 48.95833206176758
Iteration: 72. Train Loss: 0.6933581233024597. Validation Accuracy: 50.26041793823242
Iteration: 73. Train Loss: 0.6934426426887512. Validation Accuracy: 50.78125
Iteration: 74. Train Loss: 0.6932699084281921. Validation Accuracy: 50.78125
Iteration: 75. Train Loss: 0.6929827332496643. Validation Accuracy: 48.17708206176758
Iteration: 76. Train Loss: 0.6931939125061035. Validation Accuracy: 49.21875
Iteration: 77. Train Loss: 0.6929687857627869. Validation Accuracy: 49.47916793823242
Iteration: 78. Train Loss: 0.6932032108306885. Validation Accuracy: 53.38541793823242
Iteration: 79. Train Loss: 0.6930809020996094. Validation Accuracy: 51.04166793823242
Iteration: 80. Train Loss: 0.692657470703125. Validation Accuracy: 48.69791793823242
Iteration: 81. Train Loss: 0.6931094527244568. Validation Accuracy: 46.09375
Iteration: 82. Train Loss: 0.6933388113975525. Validation Accuracy: 57.03125
Iteration: 83. Train Loss: 0.6935288310050964. Validation Accuracy: 47.39583206176758
Iteration: 84. Train Loss: 0.6940248608589172. Validation Accuracy: 53.125
Iteration: 85. Train Loss: 0.6927012801170349. Validation Accuracy: 47.65625
Iteration: 86. Train Loss: 0.6929596066474915. Validation Accuracy: 45.05208206176758
Iteration: 87. Train Loss: 0.6931770443916321. Validation Accuracy: 49.47916793823242
Iteration: 88. Train Loss: 0.6938447952270508. Validation Accuracy: 56.51041793823242
Iteration: 89. Train Loss: 0.6937553882598877. Validation Accuracy: 49.73958206176758
Iteration: 90. Train Loss: 0.6921040415763855. Validation Accuracy: 48.4375
Iteration: 91. Train Loss: 0.6928973197937012. Validation Accuracy: 44.79166793823242
Iteration: 92. Train Loss: 0.6931295990943909. Validation Accuracy: 48.17708206176758
Iteration: 93. Train Loss: 0.6936982274055481. Validation Accuracy: 48.17708206176758
Iteration: 94. Train Loss: 0.6932258605957031. Validation Accuracy: 51.82291793823242
Iteration: 95. Train Loss: 0.6935905814170837. Validation Accuracy: 48.69791793823242
Iteration: 96. Train Loss: 0.6927586197853088. Validation Accuracy: 53.64583206176758
Iteration: 97. Train Loss: 0.6921973824501038. Validation Accuracy: 51.5625
Iteration: 98. Train Loss: 0.6928964257240295. Validation Accuracy: 53.90625
Iteration: 99. Train Loss: 0.6931210160255432. Validation Accuracy: 47.13541793823242
Iteration: 100. Train Loss: 0.6923887729644775. Validation Accuracy: 51.82291793823242
Iteration: 101. Train Loss: 0.6936466693878174. Validation Accuracy: 48.95833206176758
Iteration: 102. Train Loss: 0.6934642195701599. Validation Accuracy: 55.20833206176758
Iteration: 103. Train Loss: 0.6933481693267822. Validation Accuracy: 48.69791793823242
Iteration: 104. Train Loss: 0.6930409073829651. Validation Accuracy: 51.82291793823242
Iteration: 105. Train Loss: 0.6951203346252441. Validation Accuracy: 49.47916793823242
Iteration: 106. Train Loss: 0.6929900646209717. Validation Accuracy: 49.21875
Iteration: 107. Train Loss: 0.6942605972290039. Validation Accuracy: 51.04166793823242
Iteration: 108. Train Loss: 0.6942448019981384. Validation Accuracy: 55.72916793823242
Iteration: 109. Train Loss: 0.6924851536750793. Validation Accuracy: 51.30208206176758
Iteration: 110. Train Loss: 0.6937478184700012. Validation Accuracy: 52.60416793823242
Iteration: 111. Train Loss: 0.6932177543640137. Validation Accuracy: 48.95833206176758
Iteration: 112. Train Loss: 0.6930820345878601. Validation Accuracy: 47.91666793823242
Iteration: 113. Train Loss: 0.6932198405265808. Validation Accuracy: 48.4375
Iteration: 114. Train Loss: 0.6930444836616516. Validation Accuracy: 48.69791793823242
Iteration: 115. Train Loss: 0.6932413578033447. Validation Accuracy: 48.17708206176758
Iteration: 116. Train Loss: 0.6930593848228455. Validation Accuracy: 56.77083206176758
Iteration: 117. Train Loss: 0.6928492188453674. Validation Accuracy: 53.90625
Iteration: 118. Train Loss: 0.6933878064155579. Validation Accuracy: 51.04166793823242
Iteration: 119. Train Loss: 0.6930870413780212. Validation Accuracy: 47.39583206176758
Iteration: 120. Train Loss: 0.6928513646125793. Validation Accuracy: 52.86458206176758
Iteration: 121. Train Loss: 0.6931412816047668. Validation Accuracy: 48.95833206176758
Iteration: 122. Train Loss: 0.6933372616767883. Validation Accuracy: 56.25
Iteration: 123. Train Loss: 0.6932904124259949. Validation Accuracy: 50.26041793823242
Iteration: 124. Train Loss: 0.6936059594154358. Validation Accuracy: 51.82291793823242
Iteration: 125. Train Loss: 0.6930019855499268. Validation Accuracy: 54.42708206176758
Iteration: 126. Train Loss: 0.6932082176208496. Validation Accuracy: 52.08333206176758
Iteration: 127. Train Loss: 0.6928923726081848. Validation Accuracy: 47.13541793823242
Iteration: 128. Train Loss: 0.693115770816803. Validation Accuracy: 51.04166793823242
Iteration: 129. Train Loss: 0.6927357316017151. Validation Accuracy: 50.26041793823242
Iteration: 130. Train Loss: 0.6932892203330994. Validation Accuracy: 51.30208206176758
Iteration: 131. Train Loss: 0.6934614181518555. Validation Accuracy: 50.78125
Iteration: 132. Train Loss: 0.6928903460502625. Validation Accuracy: 50.78125
Iteration: 133. Train Loss: 0.6932504177093506. Validation Accuracy: 48.95833206176758
Iteration: 134. Train Loss: 0.6935208439826965. Validation Accuracy: 52.34375
Iteration: 135. Train Loss: 0.6929786205291748. Validation Accuracy: 52.34375
Iteration: 136. Train Loss: 0.6930484175682068. Validation Accuracy: 47.65625
Iteration: 137. Train Loss: 0.6933209300041199. Validation Accuracy: 49.73958206176758
Iteration: 138. Train Loss: 0.693519115447998. Validation Accuracy: 50.0
Iteration: 139. Train Loss: 0.6937665343284607. Validation Accuracy: 45.3125
Iteration: 140. Train Loss: 0.6932477355003357. Validation Accuracy: 50.26041793823242
Iteration: 141. Train Loss: 0.6933876872062683. Validation Accuracy: 53.125
Iteration: 142. Train Loss: 0.6934179663658142. Validation Accuracy: 46.35416793823242
Iteration: 143. Train Loss: 0.6929323673248291. Validation Accuracy: 50.52083206176758
Iteration: 144. Train Loss: 0.6934167742729187. Validation Accuracy: 48.69791793823242
Iteration: 145. Train Loss: 0.6932644248008728. Validation Accuracy: 52.08333206176758
Iteration: 146. Train Loss: 0.6933586597442627. Validation Accuracy: 46.875
Iteration: 147. Train Loss: 0.6933427453041077. Validation Accuracy: 47.65625
Iteration: 148. Train Loss: 0.693182647228241. Validation Accuracy: 50.52083206176758
Iteration: 149. Train Loss: 0.6932129859924316. Validation Accuracy: 51.30208206176758
Iteration: 150. Train Loss: 0.6932861804962158. Validation Accuracy: 49.73958206176758
Iteration: 151. Train Loss: 0.6935111880302429. Validation Accuracy: 48.69791793823242
Iteration: 152. Train Loss: 0.693020761013031. Validation Accuracy: 45.3125
Iteration: 153. Train Loss: 0.6932032704353333. Validation Accuracy: 51.04166793823242
Iteration: 154. Train Loss: 0.6933627724647522. Validation Accuracy: 51.04166793823242
Iteration: 155. Train Loss: 0.6932142376899719. Validation Accuracy: 50.26041793823242
Iteration: 156. Train Loss: 0.6933556199073792. Validation Accuracy: 51.82291793823242
Iteration: 157. Train Loss: 0.6930943131446838. Validation Accuracy: 45.83333206176758
Iteration: 158. Train Loss: 0.6933076977729797. Validation Accuracy: 51.04166793823242
Iteration: 159. Train Loss: 0.6930890679359436. Validation Accuracy: 45.57291793823242
Iteration: 160. Train Loss: 0.6931902766227722. Validation Accuracy: 52.34375
Iteration: 161. Train Loss: 0.6931988596916199. Validation Accuracy: 55.98958206176758
Iteration: 162. Train Loss: 0.6934393048286438. Validation Accuracy: 46.61458206176758
Iteration: 163. Train Loss: 0.6930474638938904. Validation Accuracy: 49.73958206176758
Iteration: 164. Train Loss: 0.6932231783866882. Validation Accuracy: 51.5625
Iteration: 165. Train Loss: 0.6929967403411865. Validation Accuracy: 47.13541793823242
Iteration: 166. Train Loss: 0.6931825280189514. Validation Accuracy: 48.69791793823242
Iteration: 167. Train Loss: 0.6931318640708923. Validation Accuracy: 50.0
Iteration: 168. Train Loss: 0.693098783493042. Validation Accuracy: 46.875
Iteration: 169. Train Loss: 0.6927089691162109. Validation Accuracy: 48.4375
Iteration: 170. Train Loss: 0.6926953196525574. Validation Accuracy: 53.125
Iteration: 171. Train Loss: 0.6936455368995667. Validation Accuracy: 52.34375
Iteration: 172. Train Loss: 0.6933398842811584. Validation Accuracy: 50.78125
Iteration: 173. Train Loss: 0.6935389041900635. Validation Accuracy: 47.65625
Iteration: 174. Train Loss: 0.693997859954834. Validation Accuracy: 51.82291793823242
Iteration: 175. Train Loss: 0.6939833164215088. Validation Accuracy: 47.39583206176758
Iteration: 176. Train Loss: 0.6930487155914307. Validation Accuracy: 50.0
Iteration: 177. Train Loss: 0.6931535601615906. Validation Accuracy: 51.30208206176758
Iteration: 178. Train Loss: 0.6940932273864746. Validation Accuracy: 47.65625
Iteration: 179. Train Loss: 0.6924185156822205. Validation Accuracy: 50.78125
Iteration: 180. Train Loss: 0.6932797431945801. Validation Accuracy: 50.78125
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Iteration: 4831. Train Loss: 0.5388419032096863. Validation Accuracy: 66.92708587646484
Iteration: 4832. Train Loss: 0.5504328608512878. Validation Accuracy: 67.96875
Iteration: 4833. Train Loss: 0.5400243401527405. Validation Accuracy: 63.80208206176758
Iteration: 4834. Train Loss: 0.5720314383506775. Validation Accuracy: 59.375
Iteration: 4835. Train Loss: 0.5372812151908875. Validation Accuracy: 60.41666793823242
Iteration: 4836. Train Loss: 0.5202932357788086. Validation Accuracy: 69.01041412353516
Iteration: 4837. Train Loss: 0.576259195804596. Validation Accuracy: 67.96875
Iteration: 4838. Train Loss: 0.5545862317085266. Validation Accuracy: 75.26041412353516
Iteration: 4839. Train Loss: 0.545495331287384. Validation Accuracy: 67.70833587646484
Iteration: 4840. Train Loss: 0.5619863271713257. Validation Accuracy: 63.28125
Iteration: 4841. Train Loss: 0.5440536141395569. Validation Accuracy: 69.79166412353516
Iteration: 4842. Train Loss: 0.5251739621162415. Validation Accuracy: 67.1875
Iteration: 4843. Train Loss: 0.5331985950469971. Validation Accuracy: 64.32291412353516
Iteration: 4844. Train Loss: 0.5751213431358337. Validation Accuracy: 71.09375
Iteration: 4845. Train Loss: 0.5549114346504211. Validation Accuracy: 65.36458587646484
Iteration: 4846. Train Loss: 0.553768515586853. Validation Accuracy: 68.75
Iteration: 4847. Train Loss: 0.5567198395729065. Validation Accuracy: 61.71875
Iteration: 4848. Train Loss: 0.5353524088859558. Validation Accuracy: 68.22916412353516
Iteration: 4849. Train Loss: 0.5789675712585449. Validation Accuracy: 64.32291412353516
Iteration: 4850. Train Loss: 0.5405051112174988. Validation Accuracy: 64.0625
Iteration: 4851. Train Loss: 0.545305609703064. Validation Accuracy: 66.40625
Iteration: 4852. Train Loss: 0.5498228669166565. Validation Accuracy: 69.01041412353516
Iteration: 4853. Train Loss: 0.5550312399864197. Validation Accuracy: 70.83333587646484
Iteration: 4854. Train Loss: 0.5576114058494568. Validation Accuracy: 66.40625
Iteration: 4855. Train Loss: 0.5364285707473755. Validation Accuracy: 69.79166412353516
Iteration: 4856. Train Loss: 0.5581791400909424. Validation Accuracy: 71.61458587646484
Iteration: 4857. Train Loss: 0.5421764850616455. Validation Accuracy: 70.05208587646484
Iteration: 4858. Train Loss: 0.5464057326316833. Validation Accuracy: 64.84375
Iteration: 4859. Train Loss: 0.5381194949150085. Validation Accuracy: 66.14583587646484
Iteration: 4860. Train Loss: 0.5641631484031677. Validation Accuracy: 63.28125
Iteration: 4861. Train Loss: 0.5328349471092224. Validation Accuracy: 73.95833587646484
Iteration: 4862. Train Loss: 0.565188467502594. Validation Accuracy: 66.66666412353516
Iteration: 4863. Train Loss: 0.530487596988678. Validation Accuracy: 65.10416412353516
Iteration: 4864. Train Loss: 0.5548394322395325. Validation Accuracy: 66.14583587646484
Iteration: 4865. Train Loss: 0.5470245480537415. Validation Accuracy: 61.97916793823242
Iteration: 4866. Train Loss: 0.5810732841491699. Validation Accuracy: 73.4375
Iteration: 4867. Train Loss: 0.5617921948432922. Validation Accuracy: 64.32291412353516
Iteration: 4868. Train Loss: 0.5545434355735779. Validation Accuracy: 63.02083206176758
Iteration: 4869. Train Loss: 0.5592213273048401. Validation Accuracy: 62.23958206176758
Iteration: 4870. Train Loss: 0.5457203984260559. Validation Accuracy: 57.8125
Iteration: 4871. Train Loss: 0.5666223168373108. Validation Accuracy: 67.96875
Iteration: 4872. Train Loss: 0.555637538433075. Validation Accuracy: 66.14583587646484
Iteration: 4873. Train Loss: 0.5483772158622742. Validation Accuracy: 67.44791412353516
Iteration: 4874. Train Loss: 0.5506118535995483. Validation Accuracy: 62.76041793823242
Iteration: 4875. Train Loss: 0.5524199604988098. Validation Accuracy: 73.17708587646484
Iteration: 4876. Train Loss: 0.5571326613426208. Validation Accuracy: 71.61458587646484
Iteration: 4877. Train Loss: 0.5223247408866882. Validation Accuracy: 67.70833587646484
Iteration: 4878. Train Loss: 0.5514773726463318. Validation Accuracy: 68.48958587646484
Iteration: 4879. Train Loss: 0.5418382287025452. Validation Accuracy: 64.84375
Iteration: 4880. Train Loss: 0.5475143790245056. Validation Accuracy: 70.57291412353516
Iteration: 4881. Train Loss: 0.5615448951721191. Validation Accuracy: 69.01041412353516
Iteration: 4882. Train Loss: 0.5428850650787354. Validation Accuracy: 71.09375
Iteration: 4883. Train Loss: 0.5693016648292542. Validation Accuracy: 67.44791412353516
Iteration: 4884. Train Loss: 0.5656154751777649. Validation Accuracy: 69.01041412353516
Iteration: 4885. Train Loss: 0.5654361248016357. Validation Accuracy: 69.53125
Iteration: 4886. Train Loss: 0.5718979835510254. Validation Accuracy: 59.63541793823242
Iteration: 4887. Train Loss: 0.5198610424995422. Validation Accuracy: 62.5
Iteration: 4888. Train Loss: 0.5416514277458191. Validation Accuracy: 66.92708587646484
Iteration: 4889. Train Loss: 0.5582513213157654. Validation Accuracy: 64.32291412353516
Iteration: 4890. Train Loss: 0.5359401702880859. Validation Accuracy: 65.88541412353516
Iteration: 4891. Train Loss: 0.549263596534729. Validation Accuracy: 67.44791412353516
Iteration: 4892. Train Loss: 0.5462418794631958. Validation Accuracy: 61.19791793823242
Iteration: 4893. Train Loss: 0.5325120687484741. Validation Accuracy: 66.92708587646484
Iteration: 4894. Train Loss: 0.5323969721794128. Validation Accuracy: 70.3125
Iteration: 4895. Train Loss: 0.5595924258232117. Validation Accuracy: 66.14583587646484
Iteration: 4896. Train Loss: 0.5812264084815979. Validation Accuracy: 64.58333587646484
Iteration: 4897. Train Loss: 0.5346964001655579. Validation Accuracy: 67.44791412353516
Iteration: 4898. Train Loss: 0.5493645071983337. Validation Accuracy: 65.625
Iteration: 4899. Train Loss: 0.5312882661819458. Validation Accuracy: 63.02083206176758
Iteration: 4900. Train Loss: 0.5402936339378357. Validation Accuracy: 67.1875
Iteration: 4901. Train Loss: 0.5576804280281067. Validation Accuracy: 64.32291412353516
Iteration: 4902. Train Loss: 0.553133487701416. Validation Accuracy: 63.02083206176758
Iteration: 4903. Train Loss: 0.5495519042015076. Validation Accuracy: 64.84375
Iteration: 4904. Train Loss: 0.5620771050453186. Validation Accuracy: 66.40625
Iteration: 4905. Train Loss: 0.5594974756240845. Validation Accuracy: 69.01041412353516
Iteration: 4906. Train Loss: 0.5308361649513245. Validation Accuracy: 65.36458587646484
Iteration: 4907. Train Loss: 0.5339776277542114. Validation Accuracy: 70.57291412353516
Iteration: 4908. Train Loss: 0.5261947512626648. Validation Accuracy: 69.53125
Iteration: 4909. Train Loss: 0.5476668477058411. Validation Accuracy: 67.96875
Iteration: 4910. Train Loss: 0.5737531185150146. Validation Accuracy: 65.36458587646484
Iteration: 4911. Train Loss: 0.5453965067863464. Validation Accuracy: 62.76041793823242
Iteration: 4912. Train Loss: 0.545805037021637. Validation Accuracy: 66.14583587646484
Iteration: 4913. Train Loss: 0.545853316783905. Validation Accuracy: 66.92708587646484
Iteration: 4914. Train Loss: 0.5581640601158142. Validation Accuracy: 66.66666412353516
Iteration: 4915. Train Loss: 0.5536778569221497. Validation Accuracy: 63.80208206176758
Iteration: 4916. Train Loss: 0.5314387083053589. Validation Accuracy: 66.40625
Iteration: 4917. Train Loss: 0.576431930065155. Validation Accuracy: 63.54166793823242
Iteration: 4918. Train Loss: 0.5288928151130676. Validation Accuracy: 69.79166412353516
Iteration: 4919. Train Loss: 0.5619010925292969. Validation Accuracy: 64.84375
Iteration: 4920. Train Loss: 0.5636009573936462. Validation Accuracy: 63.02083206176758
Iteration: 4921. Train Loss: 0.5572216510772705. Validation Accuracy: 65.10416412353516
Iteration: 4922. Train Loss: 0.5298869609832764. Validation Accuracy: 64.58333587646484
Iteration: 4923. Train Loss: 0.5559414029121399. Validation Accuracy: 66.40625
Iteration: 4924. Train Loss: 0.5694804787635803. Validation Accuracy: 68.22916412353516
Iteration: 4925. Train Loss: 0.5502592325210571. Validation Accuracy: 67.44791412353516
Iteration: 4926. Train Loss: 0.5585031509399414. Validation Accuracy: 70.57291412353516
Iteration: 4927. Train Loss: 0.5385597944259644. Validation Accuracy: 69.79166412353516
Iteration: 4928. Train Loss: 0.5389326810836792. Validation Accuracy: 69.01041412353516
Iteration: 4929. Train Loss: 0.5474392175674438. Validation Accuracy: 66.14583587646484
Iteration: 4930. Train Loss: 0.528741180896759. Validation Accuracy: 66.14583587646484
Iteration: 4931. Train Loss: 0.5529427528381348. Validation Accuracy: 63.02083206176758
Iteration: 4932. Train Loss: 0.5637007355690002. Validation Accuracy: 62.23958206176758
Iteration: 4933. Train Loss: 0.5503791570663452. Validation Accuracy: 65.625
Iteration: 4934. Train Loss: 0.5348042845726013. Validation Accuracy: 65.36458587646484
Iteration: 4935. Train Loss: 0.5636870265007019. Validation Accuracy: 67.1875
Iteration: 4936. Train Loss: 0.5752586126327515. Validation Accuracy: 66.66666412353516
Iteration: 4937. Train Loss: 0.5485798716545105. Validation Accuracy: 70.3125
Iteration: 4938. Train Loss: 0.5628511905670166. Validation Accuracy: 69.79166412353516
Iteration: 4939. Train Loss: 0.5592448115348816. Validation Accuracy: 60.41666793823242
Iteration: 4940. Train Loss: 0.5421993732452393. Validation Accuracy: 61.45833206176758
Iteration: 4941. Train Loss: 0.5420034527778625. Validation Accuracy: 65.36458587646484
Iteration: 4942. Train Loss: 0.5516753792762756. Validation Accuracy: 72.65625
Iteration: 4943. Train Loss: 0.5793842077255249. Validation Accuracy: 69.01041412353516
Iteration: 4944. Train Loss: 0.5743471384048462. Validation Accuracy: 61.19791793823242
Iteration: 4945. Train Loss: 0.5506646037101746. Validation Accuracy: 68.75
Iteration: 4946. Train Loss: 0.5595206618309021. Validation Accuracy: 69.27083587646484
Iteration: 4947. Train Loss: 0.5527325868606567. Validation Accuracy: 67.1875
Iteration: 4948. Train Loss: 0.5446736216545105. Validation Accuracy: 61.45833206176758
Iteration: 4949. Train Loss: 0.5587530732154846. Validation Accuracy: 64.84375
Iteration: 4950. Train Loss: 0.5510122179985046. Validation Accuracy: 69.53125
Iteration: 4951. Train Loss: 0.5494517683982849. Validation Accuracy: 68.22916412353516
Iteration: 4952. Train Loss: 0.5576920509338379. Validation Accuracy: 67.70833587646484
Iteration: 4953. Train Loss: 0.5471730828285217. Validation Accuracy: 67.1875
Iteration: 4954. Train Loss: 0.5374090075492859. Validation Accuracy: 73.69791412353516
Iteration: 4955. Train Loss: 0.5527592897415161. Validation Accuracy: 71.09375
Iteration: 4956. Train Loss: 0.5378316640853882. Validation Accuracy: 67.70833587646484
Iteration: 4957. Train Loss: 0.5427133440971375. Validation Accuracy: 65.625
Iteration: 4958. Train Loss: 0.5390158295631409. Validation Accuracy: 66.14583587646484
Iteration: 4959. Train Loss: 0.5443830490112305. Validation Accuracy: 66.92708587646484
Iteration: 4960. Train Loss: 0.5451955199241638. Validation Accuracy: 65.625
Iteration: 4961. Train Loss: 0.5589925646781921. Validation Accuracy: 67.96875
Iteration: 4962. Train Loss: 0.5944986939430237. Validation Accuracy: 65.88541412353516
Iteration: 4963. Train Loss: 0.5356808304786682. Validation Accuracy: 72.65625
Iteration: 4964. Train Loss: 0.5644752383232117. Validation Accuracy: 67.1875
Iteration: 4965. Train Loss: 0.5608872175216675. Validation Accuracy: 69.53125
Iteration: 4966. Train Loss: 0.5518056750297546. Validation Accuracy: 68.75
Iteration: 4967. Train Loss: 0.5368580222129822. Validation Accuracy: 69.01041412353516
Iteration: 4968. Train Loss: 0.5359423756599426. Validation Accuracy: 69.01041412353516
Iteration: 4969. Train Loss: 0.553351104259491. Validation Accuracy: 72.65625
Iteration: 4970. Train Loss: 0.5602012276649475. Validation Accuracy: 63.28125
Iteration: 4971. Train Loss: 0.5408561825752258. Validation Accuracy: 67.96875
Iteration: 4972. Train Loss: 0.5777783393859863. Validation Accuracy: 67.44791412353516
Iteration: 4973. Train Loss: 0.5667458176612854. Validation Accuracy: 60.9375
Iteration: 4974. Train Loss: 0.5450107455253601. Validation Accuracy: 69.27083587646484
Iteration: 4975. Train Loss: 0.5641796588897705. Validation Accuracy: 67.70833587646484
Iteration: 4976. Train Loss: 0.5456743240356445. Validation Accuracy: 63.80208206176758
Iteration: 4977. Train Loss: 0.55405193567276. Validation Accuracy: 64.84375
Iteration: 4978. Train Loss: 0.5474147796630859. Validation Accuracy: 70.57291412353516
Iteration: 4979. Train Loss: 0.5592952966690063. Validation Accuracy: 65.10416412353516
Iteration: 4980. Train Loss: 0.5471215844154358. Validation Accuracy: 69.01041412353516
Iteration: 4981. Train Loss: 0.5404631495475769. Validation Accuracy: 71.875
Iteration: 4982. Train Loss: 0.5400235056877136. Validation Accuracy: 62.5
Iteration: 4983. Train Loss: 0.5489179491996765. Validation Accuracy: 70.05208587646484
Iteration: 4984. Train Loss: 0.5504639148712158. Validation Accuracy: 66.92708587646484
Iteration: 4985. Train Loss: 0.5452166199684143. Validation Accuracy: 65.88541412353516
Iteration: 4986. Train Loss: 0.5681732296943665. Validation Accuracy: 53.125
Iteration: 4987. Train Loss: 0.5714178681373596. Validation Accuracy: 69.53125
Iteration: 4988. Train Loss: 0.5585387945175171. Validation Accuracy: 67.1875
Iteration: 4989. Train Loss: 0.550507128238678. Validation Accuracy: 65.625
Iteration: 4990. Train Loss: 0.5646246671676636. Validation Accuracy: 72.65625
Iteration: 4991. Train Loss: 0.5755830407142639. Validation Accuracy: 63.02083206176758
Iteration: 4992. Train Loss: 0.5175415873527527. Validation Accuracy: 63.80208206176758
Iteration: 4993. Train Loss: 0.5432991981506348. Validation Accuracy: 70.3125
Iteration: 4994. Train Loss: 0.5500239729881287. Validation Accuracy: 64.0625
Iteration: 4995. Train Loss: 0.5482608675956726. Validation Accuracy: 65.88541412353516
Iteration: 4996. Train Loss: 0.5668046474456787. Validation Accuracy: 64.58333587646484
Iteration: 4997. Train Loss: 0.5394649505615234. Validation Accuracy: 67.1875
Iteration: 4998. Train Loss: 0.5703967809677124. Validation Accuracy: 65.625
Iteration: 4999. Train Loss: 0.5496209859848022. Validation Accuracy: 73.95833587646484
Finished Training
Learning curves¶
In [ ]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [ ]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Accuracy vs L¶
In [ ]:
acc = []
for L in range(1, 21):
inputs, labels = get_batch(100, L)
outputs = model(inputs)
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
acc.append(accuracy_score(labels.flatten().numpy(), predicted.numpy()))
In [ ]:
# Plot the val accuracy
plt.plot(range(1, 21), acc)
plt.ylabel('Accuracy')
plt.xlabel('Sequence length - L')
plt.title('Accuracy vs L plot')
plt.show()
Hidden state size = 10¶
In [ ]:
INPUT_DIM = 2
HIDDEN_DIM = 10
OUTPUT_DIM = 2
NUM_LAYERS = 5
LR = 0.001
K = 64
L = 5
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [ ]:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [ ]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for iteration in range(5000):
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs.view(-1, 2), labels)
loss.backward()
optimizer.step()
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# Forward pass only to get logits/output
outputs = model(inputs)
val_cost = criterion(outputs.view(-1, 2), labels)
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(iteration, loss.item(), accuracy))
print('Finished Training')
Streaming output truncated to the last 5000 lines.
Iteration: 1. Train Loss: 0.6985061764717102. Validation Accuracy: 48.17708206176758
Iteration: 2. Train Loss: 0.7001374363899231. Validation Accuracy: 47.13541793823242
Iteration: 3. Train Loss: 0.6970900893211365. Validation Accuracy: 49.73958206176758
Iteration: 4. Train Loss: 0.69525545835495. Validation Accuracy: 50.78125
Iteration: 5. Train Loss: 0.6918273568153381. Validation Accuracy: 53.90625
Iteration: 6. Train Loss: 0.6947813630104065. Validation Accuracy: 48.69791793823242
Iteration: 7. Train Loss: 0.6952977776527405. Validation Accuracy: 52.86458206176758
Iteration: 8. Train Loss: 0.6904236674308777. Validation Accuracy: 53.90625
Iteration: 9. Train Loss: 0.6947624683380127. Validation Accuracy: 50.0
Iteration: 10. Train Loss: 0.6945505738258362. Validation Accuracy: 50.78125
Iteration: 11. Train Loss: 0.6947948336601257. Validation Accuracy: 48.95833206176758
Iteration: 12. Train Loss: 0.6943961977958679. Validation Accuracy: 46.61458206176758
Iteration: 13. Train Loss: 0.6919853091239929. Validation Accuracy: 53.90625
Iteration: 14. Train Loss: 0.6937747597694397. Validation Accuracy: 47.91666793823242
Iteration: 15. Train Loss: 0.6914026737213135. Validation Accuracy: 48.4375
Iteration: 16. Train Loss: 0.6951265931129456. Validation Accuracy: 52.08333206176758
Iteration: 17. Train Loss: 0.6911981701850891. Validation Accuracy: 49.21875
Iteration: 18. Train Loss: 0.6915835738182068. Validation Accuracy: 47.39583206176758
Iteration: 19. Train Loss: 0.6932365298271179. Validation Accuracy: 50.0
Iteration: 20. Train Loss: 0.6940625309944153. Validation Accuracy: 48.17708206176758
Iteration: 21. Train Loss: 0.6924379467964172. Validation Accuracy: 49.47916793823242
Iteration: 22. Train Loss: 0.6946761608123779. Validation Accuracy: 45.57291793823242
Iteration: 23. Train Loss: 0.6932077407836914. Validation Accuracy: 48.95833206176758
Iteration: 24. Train Loss: 0.6915236115455627. Validation Accuracy: 53.64583206176758
Iteration: 25. Train Loss: 0.6930887699127197. Validation Accuracy: 53.64583206176758
Iteration: 26. Train Loss: 0.6923374533653259. Validation Accuracy: 49.21875
Iteration: 27. Train Loss: 0.6920318603515625. Validation Accuracy: 49.73958206176758
Iteration: 28. Train Loss: 0.6937814354896545. Validation Accuracy: 46.35416793823242
Iteration: 29. Train Loss: 0.6928532719612122. Validation Accuracy: 50.52083206176758
Iteration: 30. Train Loss: 0.691392183303833. Validation Accuracy: 47.91666793823242
Iteration: 31. Train Loss: 0.6936593651771545. Validation Accuracy: 50.0
Iteration: 32. Train Loss: 0.6938393712043762. Validation Accuracy: 48.69791793823242
Iteration: 33. Train Loss: 0.6916603446006775. Validation Accuracy: 51.30208206176758
Iteration: 34. Train Loss: 0.6909253597259521. Validation Accuracy: 51.30208206176758
Iteration: 35. Train Loss: 0.6927085518836975. Validation Accuracy: 53.90625
Iteration: 36. Train Loss: 0.6934922337532043. Validation Accuracy: 50.26041793823242
Iteration: 37. Train Loss: 0.6936792731285095. Validation Accuracy: 47.39583206176758
Iteration: 38. Train Loss: 0.6934247016906738. Validation Accuracy: 48.69791793823242
Iteration: 39. Train Loss: 0.6910264492034912. Validation Accuracy: 51.5625
Iteration: 40. Train Loss: 0.693845808506012. Validation Accuracy: 47.65625
Iteration: 41. Train Loss: 0.6921470165252686. Validation Accuracy: 48.4375
Iteration: 42. Train Loss: 0.6907691955566406. Validation Accuracy: 46.875
Iteration: 43. Train Loss: 0.6934409737586975. Validation Accuracy: 47.39583206176758
Iteration: 44. Train Loss: 0.6934995055198669. Validation Accuracy: 50.78125
Iteration: 45. Train Loss: 0.6923606991767883. Validation Accuracy: 52.34375
Iteration: 46. Train Loss: 0.6926687359809875. Validation Accuracy: 49.47916793823242
Iteration: 47. Train Loss: 0.6933825612068176. Validation Accuracy: 48.69791793823242
Iteration: 48. Train Loss: 0.6918572783470154. Validation Accuracy: 49.73958206176758
Iteration: 49. Train Loss: 0.6931009888648987. Validation Accuracy: 50.0
Iteration: 50. Train Loss: 0.6925745606422424. Validation Accuracy: 49.73958206176758
Iteration: 51. Train Loss: 0.6933913826942444. Validation Accuracy: 54.16666793823242
Iteration: 52. Train Loss: 0.693753182888031. Validation Accuracy: 46.875
Iteration: 53. Train Loss: 0.6934906840324402. Validation Accuracy: 50.52083206176758
Iteration: 54. Train Loss: 0.6948384642601013. Validation Accuracy: 46.35416793823242
Iteration: 55. Train Loss: 0.6918540000915527. Validation Accuracy: 50.0
Iteration: 56. Train Loss: 0.6928644180297852. Validation Accuracy: 52.86458206176758
Iteration: 57. Train Loss: 0.6945414543151855. Validation Accuracy: 50.52083206176758
Iteration: 58. Train Loss: 0.6888534426689148. Validation Accuracy: 47.91666793823242
Iteration: 59. Train Loss: 0.694101095199585. Validation Accuracy: 50.52083206176758
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Iteration: 4978. Train Loss: 0.5552103519439697. Validation Accuracy: 70.57291412353516
Iteration: 4979. Train Loss: 0.5693241953849792. Validation Accuracy: 62.5
Iteration: 4980. Train Loss: 0.5358851552009583. Validation Accuracy: 59.89583206176758
Iteration: 4981. Train Loss: 0.5618798136711121. Validation Accuracy: 71.09375
Iteration: 4982. Train Loss: 0.5577229857444763. Validation Accuracy: 59.11458206176758
Iteration: 4983. Train Loss: 0.5543372631072998. Validation Accuracy: 72.91666412353516
Iteration: 4984. Train Loss: 0.5272631049156189. Validation Accuracy: 66.92708587646484
Iteration: 4985. Train Loss: 0.5844465494155884. Validation Accuracy: 73.4375
Iteration: 4986. Train Loss: 0.567648708820343. Validation Accuracy: 67.96875
Iteration: 4987. Train Loss: 0.5478436946868896. Validation Accuracy: 70.57291412353516
Iteration: 4988. Train Loss: 0.5336164236068726. Validation Accuracy: 62.76041793823242
Iteration: 4989. Train Loss: 0.5437887907028198. Validation Accuracy: 68.75
Iteration: 4990. Train Loss: 0.5302497148513794. Validation Accuracy: 62.76041793823242
Iteration: 4991. Train Loss: 0.5539125800132751. Validation Accuracy: 68.22916412353516
Iteration: 4992. Train Loss: 0.5603641867637634. Validation Accuracy: 68.22916412353516
Iteration: 4993. Train Loss: 0.5609140396118164. Validation Accuracy: 65.10416412353516
Iteration: 4994. Train Loss: 0.5567051768302917. Validation Accuracy: 67.44791412353516
Iteration: 4995. Train Loss: 0.5400324463844299. Validation Accuracy: 65.88541412353516
Iteration: 4996. Train Loss: 0.5385661721229553. Validation Accuracy: 75.78125
Iteration: 4997. Train Loss: 0.5508840680122375. Validation Accuracy: 68.48958587646484
Iteration: 4998. Train Loss: 0.5674200654029846. Validation Accuracy: 66.92708587646484
Iteration: 4999. Train Loss: 0.5509069561958313. Validation Accuracy: 68.48958587646484
Finished Training
Learning curves¶
In [ ]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [ ]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Accuracy vs L¶
In [ ]:
acc = []
for L in range(1, 21):
inputs, labels = get_batch(100, L)
outputs = model(inputs)
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
acc.append(accuracy_score(labels.flatten().numpy(), predicted.numpy()))
In [ ]:
# Plot the val accuracy
plt.plot(range(1, 21), acc)
plt.ylabel('Accuracy')
plt.xlabel('Sequence length - L')
plt.title('Accuracy vs L plot')
plt.show()
When we vary the state vector size (2, 5, 10), the average accuracy remains more or less the same with no significant gains
MSE Loss vs Cross Entropy¶
In [ ]:
class RNN(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, layers = 1):
super().__init__()
self.layers = layers
self.hidden_dim = hidden_dim
self.rnn = nn.LSTM(input_dim, hidden_dim, num_layers = layers, batch_first = True)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, text):
#text = [sent len, batch size]
#embedded = [sent len, batch size, emb dim]
output, (hidden, cell) = self.rnn(text)
#output = [sent len, batch size, hid dim]
#hidden = [1, batch size, hid dim]
# assert torch.equal(output[-1,:,:], hidden.squeeze(0))
# hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)
return F.softmax(self.fc(output), dim = -1)
In [ ]:
INPUT_DIM = 2
HIDDEN_DIM = 5
OUTPUT_DIM = 2
NUM_LAYERS = 5
LR = 0.001
K = 64
L = 5
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [ ]:
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [ ]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for iteration in range(5000):
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs.view(-1, 2)[:, -1], labels.float())
loss.backward()
optimizer.step()
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# Forward pass only to get logits/output
outputs = model(inputs)
val_cost = criterion(outputs.view(-1, 2)[:, -1], labels.float())
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(iteration, loss.item(), accuracy))
print('Finished Training')
Streaming output truncated to the last 5000 lines.
Iteration: 1. Train Loss: 0.2527525722980499. Validation Accuracy: 46.35416793823242
Iteration: 2. Train Loss: 0.2508527934551239. Validation Accuracy: 47.39583206176758
Iteration: 3. Train Loss: 0.2529959976673126. Validation Accuracy: 48.17708206176758
Iteration: 4. Train Loss: 0.25171905755996704. Validation Accuracy: 49.47916793823242
Iteration: 5. Train Loss: 0.25061464309692383. Validation Accuracy: 48.17708206176758
Iteration: 6. Train Loss: 0.2512994408607483. Validation Accuracy: 51.82291793823242
Iteration: 7. Train Loss: 0.25148963928222656. Validation Accuracy: 52.08333206176758
Iteration: 8. Train Loss: 0.25171810388565063. Validation Accuracy: 50.26041793823242
Iteration: 9. Train Loss: 0.25158411264419556. Validation Accuracy: 48.69791793823242
Iteration: 10. Train Loss: 0.2510802149772644. Validation Accuracy: 49.47916793823242
Iteration: 11. Train Loss: 0.2521497905254364. Validation Accuracy: 51.30208206176758
Iteration: 12. Train Loss: 0.2514175474643707. Validation Accuracy: 49.21875
Iteration: 13. Train Loss: 0.25156140327453613. Validation Accuracy: 52.60416793823242
Iteration: 14. Train Loss: 0.24910086393356323. Validation Accuracy: 52.08333206176758
Iteration: 15. Train Loss: 0.2506890296936035. Validation Accuracy: 49.73958206176758
Iteration: 16. Train Loss: 0.25271376967430115. Validation Accuracy: 48.4375
Iteration: 17. Train Loss: 0.252184122800827. Validation Accuracy: 51.30208206176758
Iteration: 18. Train Loss: 0.25265416502952576. Validation Accuracy: 49.21875
Iteration: 19. Train Loss: 0.25258490443229675. Validation Accuracy: 48.17708206176758
Iteration: 20. Train Loss: 0.2517729103565216. Validation Accuracy: 52.60416793823242
Iteration: 21. Train Loss: 0.25063613057136536. Validation Accuracy: 53.64583206176758
Iteration: 22. Train Loss: 0.2516256272792816. Validation Accuracy: 48.4375
Iteration: 23. Train Loss: 0.25005707144737244. Validation Accuracy: 51.5625
Iteration: 24. Train Loss: 0.25109875202178955. Validation Accuracy: 49.73958206176758
Iteration: 25. Train Loss: 0.2514631748199463. Validation Accuracy: 50.0
Iteration: 26. Train Loss: 0.25174853205680847. Validation Accuracy: 49.73958206176758
Iteration: 27. Train Loss: 0.25037890672683716. Validation Accuracy: 46.875
Iteration: 28. Train Loss: 0.2512962520122528. Validation Accuracy: 51.82291793823242
Iteration: 29. Train Loss: 0.2504468560218811. Validation Accuracy: 49.21875
Iteration: 30. Train Loss: 0.25042811036109924. Validation Accuracy: 52.08333206176758
Iteration: 31. Train Loss: 0.2504129111766815. Validation Accuracy: 48.95833206176758
Iteration: 32. Train Loss: 0.25014564394950867. Validation Accuracy: 48.17708206176758
Iteration: 33. Train Loss: 0.25109219551086426. Validation Accuracy: 50.52083206176758
Iteration: 34. Train Loss: 0.2500813901424408. Validation Accuracy: 48.69791793823242
Iteration: 35. Train Loss: 0.2499222308397293. Validation Accuracy: 45.57291793823242
Iteration: 36. Train Loss: 0.2502403259277344. Validation Accuracy: 50.78125
Iteration: 37. Train Loss: 0.2507169842720032. Validation Accuracy: 50.78125
Iteration: 38. Train Loss: 0.25006335973739624. Validation Accuracy: 48.69791793823242
Iteration: 39. Train Loss: 0.250247597694397. Validation Accuracy: 51.30208206176758
Iteration: 40. Train Loss: 0.2503966987133026. Validation Accuracy: 48.17708206176758
Iteration: 41. Train Loss: 0.2502875030040741. Validation Accuracy: 47.65625
Iteration: 42. Train Loss: 0.24926358461380005. Validation Accuracy: 48.4375
Iteration: 43. Train Loss: 0.2502725124359131. Validation Accuracy: 45.83333206176758
Iteration: 44. Train Loss: 0.25015950202941895. Validation Accuracy: 52.86458206176758
Iteration: 45. Train Loss: 0.24998939037322998. Validation Accuracy: 50.26041793823242
Iteration: 46. Train Loss: 0.24918930232524872. Validation Accuracy: 48.69791793823242
Iteration: 47. Train Loss: 0.2498820573091507. Validation Accuracy: 54.94791793823242
Iteration: 48. Train Loss: 0.24999450147151947. Validation Accuracy: 51.5625
Iteration: 49. Train Loss: 0.2502584457397461. Validation Accuracy: 47.13541793823242
Iteration: 50. Train Loss: 0.2505633234977722. Validation Accuracy: 48.17708206176758
Iteration: 51. Train Loss: 0.24999527633190155. Validation Accuracy: 49.47916793823242
Iteration: 52. Train Loss: 0.2503906190395355. Validation Accuracy: 50.52083206176758
Iteration: 53. Train Loss: 0.2500344216823578. Validation Accuracy: 49.47916793823242
Iteration: 54. Train Loss: 0.24969761073589325. Validation Accuracy: 49.73958206176758
Iteration: 55. Train Loss: 0.25019165873527527. Validation Accuracy: 48.95833206176758
Iteration: 56. Train Loss: 0.25103822350502014. Validation Accuracy: 46.09375
Iteration: 57. Train Loss: 0.2499762326478958. Validation Accuracy: 51.5625
Iteration: 58. Train Loss: 0.25045427680015564. Validation Accuracy: 45.3125
Iteration: 59. Train Loss: 0.25023555755615234. Validation Accuracy: 48.17708206176758
Iteration: 60. Train Loss: 0.25049930810928345. Validation Accuracy: 51.5625
Iteration: 61. Train Loss: 0.25034821033477783. Validation Accuracy: 47.91666793823242
Iteration: 62. Train Loss: 0.2502242624759674. Validation Accuracy: 48.17708206176758
Iteration: 63. Train Loss: 0.25022944808006287. Validation Accuracy: 52.60416793823242
Iteration: 64. Train Loss: 0.2503591477870941. Validation Accuracy: 49.47916793823242
Iteration: 65. Train Loss: 0.25034162402153015. Validation Accuracy: 45.05208206176758
Iteration: 66. Train Loss: 0.25048908591270447. Validation Accuracy: 49.21875
Iteration: 67. Train Loss: 0.2503303587436676. Validation Accuracy: 51.04166793823242
Iteration: 68. Train Loss: 0.25053730607032776. Validation Accuracy: 50.26041793823242
Iteration: 69. Train Loss: 0.24987687170505524. Validation Accuracy: 48.17708206176758
Iteration: 70. Train Loss: 0.24992047250270844. Validation Accuracy: 46.35416793823242
Iteration: 71. Train Loss: 0.25027188658714294. Validation Accuracy: 48.17708206176758
Iteration: 72. Train Loss: 0.2510842978954315. Validation Accuracy: 48.95833206176758
Iteration: 73. Train Loss: 0.24971537292003632. Validation Accuracy: 49.21875
Iteration: 74. Train Loss: 0.249888077378273. Validation Accuracy: 45.57291793823242
Iteration: 75. Train Loss: 0.24994497001171112. Validation Accuracy: 48.69791793823242
Iteration: 76. Train Loss: 0.250562459230423. Validation Accuracy: 47.91666793823242
Iteration: 77. Train Loss: 0.25053200125694275. Validation Accuracy: 51.5625
Iteration: 78. Train Loss: 0.25135716795921326. Validation Accuracy: 47.65625
Iteration: 79. Train Loss: 0.25044938921928406. Validation Accuracy: 48.4375
Iteration: 80. Train Loss: 0.2509455680847168. Validation Accuracy: 48.17708206176758
Iteration: 81. Train Loss: 0.25000664591789246. Validation Accuracy: 50.78125
Iteration: 82. Train Loss: 0.25024309754371643. Validation Accuracy: 46.875
Iteration: 83. Train Loss: 0.250345379114151. Validation Accuracy: 50.26041793823242
Iteration: 84. Train Loss: 0.25020480155944824. Validation Accuracy: 46.875
Iteration: 85. Train Loss: 0.24974320828914642. Validation Accuracy: 43.75
Iteration: 86. Train Loss: 0.2505982220172882. Validation Accuracy: 51.5625
Iteration: 87. Train Loss: 0.2503039538860321. Validation Accuracy: 47.13541793823242
Iteration: 88. Train Loss: 0.24997730553150177. Validation Accuracy: 52.08333206176758
Iteration: 89. Train Loss: 0.24985705316066742. Validation Accuracy: 51.5625
Iteration: 90. Train Loss: 0.24991373717784882. Validation Accuracy: 48.4375
Iteration: 91. Train Loss: 0.2502206861972809. Validation Accuracy: 51.30208206176758
Iteration: 92. Train Loss: 0.24961937963962555. Validation Accuracy: 49.73958206176758
Iteration: 93. Train Loss: 0.25016888976097107. Validation Accuracy: 47.39583206176758
Iteration: 94. Train Loss: 0.25019070506095886. Validation Accuracy: 48.95833206176758
Iteration: 95. Train Loss: 0.2499041110277176. Validation Accuracy: 41.92708206176758
Iteration: 96. Train Loss: 0.25010696053504944. Validation Accuracy: 48.17708206176758
Iteration: 97. Train Loss: 0.25048312544822693. Validation Accuracy: 49.21875
Iteration: 98. Train Loss: 0.24982452392578125. Validation Accuracy: 47.13541793823242
Iteration: 99. Train Loss: 0.2501329481601715. Validation Accuracy: 50.52083206176758
Iteration: 100. Train Loss: 0.24970810115337372. Validation Accuracy: 51.04166793823242
Iteration: 101. Train Loss: 0.2501806914806366. Validation Accuracy: 53.38541793823242
Iteration: 102. Train Loss: 0.25027963519096375. Validation Accuracy: 48.4375
Iteration: 103. Train Loss: 0.25017404556274414. Validation Accuracy: 50.78125
Iteration: 104. Train Loss: 0.2504437267780304. Validation Accuracy: 46.35416793823242
Iteration: 105. Train Loss: 0.2502391040325165. Validation Accuracy: 49.73958206176758
Iteration: 106. Train Loss: 0.25000515580177307. Validation Accuracy: 47.65625
Iteration: 107. Train Loss: 0.2501751184463501. Validation Accuracy: 44.01041793823242
Iteration: 108. Train Loss: 0.2497832328081131. Validation Accuracy: 49.21875
Iteration: 109. Train Loss: 0.24987168610095978. Validation Accuracy: 50.26041793823242
Iteration: 110. Train Loss: 0.24979697167873383. Validation Accuracy: 47.13541793823242
Iteration: 111. Train Loss: 0.25059595704078674. Validation Accuracy: 54.94791793823242
Iteration: 112. Train Loss: 0.25025036931037903. Validation Accuracy: 52.08333206176758
Iteration: 113. Train Loss: 0.2502044439315796. Validation Accuracy: 51.04166793823242
Iteration: 114. Train Loss: 0.24985958635807037. Validation Accuracy: 51.04166793823242
Iteration: 115. Train Loss: 0.25041231513023376. Validation Accuracy: 51.30208206176758
Iteration: 116. Train Loss: 0.25033488869667053. Validation Accuracy: 50.78125
Iteration: 117. Train Loss: 0.2503345310688019. Validation Accuracy: 47.65625
Iteration: 118. Train Loss: 0.2509233355522156. Validation Accuracy: 51.04166793823242
Iteration: 119. Train Loss: 0.250456839799881. Validation Accuracy: 48.4375
Iteration: 120. Train Loss: 0.250396728515625. Validation Accuracy: 47.13541793823242
Iteration: 121. Train Loss: 0.25041714310646057. Validation Accuracy: 52.34375
Iteration: 122. Train Loss: 0.2502206265926361. Validation Accuracy: 51.04166793823242
Iteration: 123. Train Loss: 0.25053003430366516. Validation Accuracy: 45.3125
Iteration: 124. Train Loss: 0.24982689321041107. Validation Accuracy: 45.05208206176758
Iteration: 125. Train Loss: 0.24999062716960907. Validation Accuracy: 51.5625
Iteration: 126. Train Loss: 0.2505089342594147. Validation Accuracy: 47.13541793823242
Iteration: 127. Train Loss: 0.2503012716770172. Validation Accuracy: 50.0
Iteration: 128. Train Loss: 0.25020143389701843. Validation Accuracy: 50.52083206176758
Iteration: 129. Train Loss: 0.24957095086574554. Validation Accuracy: 47.13541793823242
Iteration: 130. Train Loss: 0.2503122091293335. Validation Accuracy: 50.26041793823242
Iteration: 131. Train Loss: 0.2504481375217438. Validation Accuracy: 48.95833206176758
Iteration: 132. Train Loss: 0.2502707540988922. Validation Accuracy: 51.04166793823242
Iteration: 133. Train Loss: 0.24993526935577393. Validation Accuracy: 51.5625
Iteration: 134. Train Loss: 0.25020015239715576. Validation Accuracy: 49.21875
Iteration: 135. Train Loss: 0.2501427233219147. Validation Accuracy: 51.5625
Iteration: 136. Train Loss: 0.25029364228248596. Validation Accuracy: 53.38541793823242
Iteration: 137. Train Loss: 0.25053611397743225. Validation Accuracy: 52.34375
Iteration: 138. Train Loss: 0.24991345405578613. Validation Accuracy: 46.61458206176758
Iteration: 139. Train Loss: 0.2502424120903015. Validation Accuracy: 47.91666793823242
Iteration: 140. Train Loss: 0.2501090168952942. Validation Accuracy: 50.52083206176758
Iteration: 141. Train Loss: 0.2498062700033188. Validation Accuracy: 51.82291793823242
Iteration: 142. Train Loss: 0.250808984041214. Validation Accuracy: 52.08333206176758
Iteration: 143. Train Loss: 0.25007757544517517. Validation Accuracy: 50.78125
Iteration: 144. Train Loss: 0.25026002526283264. Validation Accuracy: 52.08333206176758
Iteration: 145. Train Loss: 0.2504740059375763. Validation Accuracy: 49.73958206176758
Iteration: 146. Train Loss: 0.2507786750793457. Validation Accuracy: 46.09375
Iteration: 147. Train Loss: 0.2498357743024826. Validation Accuracy: 48.95833206176758
Iteration: 148. Train Loss: 0.24991567432880402. Validation Accuracy: 48.69791793823242
Iteration: 149. Train Loss: 0.2500709891319275. Validation Accuracy: 48.95833206176758
Iteration: 150. Train Loss: 0.25055477023124695. Validation Accuracy: 50.0
Iteration: 151. Train Loss: 0.25016283988952637. Validation Accuracy: 50.26041793823242
Iteration: 152. Train Loss: 0.2498689889907837. Validation Accuracy: 48.17708206176758
Iteration: 153. Train Loss: 0.2499418705701828. Validation Accuracy: 47.65625
Iteration: 154. Train Loss: 0.25070008635520935. Validation Accuracy: 53.125
Iteration: 155. Train Loss: 0.2501409351825714. Validation Accuracy: 50.0
Iteration: 156. Train Loss: 0.24997614324092865. Validation Accuracy: 48.69791793823242
Iteration: 157. Train Loss: 0.2498982697725296. Validation Accuracy: 47.65625
Iteration: 158. Train Loss: 0.2502391040325165. Validation Accuracy: 48.69791793823242
Iteration: 159. Train Loss: 0.2498323768377304. Validation Accuracy: 48.95833206176758
Iteration: 160. Train Loss: 0.24971407651901245. Validation Accuracy: 46.875
Iteration: 161. Train Loss: 0.2503722310066223. Validation Accuracy: 45.3125
Iteration: 162. Train Loss: 0.2501644790172577. Validation Accuracy: 45.83333206176758
Iteration: 163. Train Loss: 0.2502233684062958. Validation Accuracy: 53.125
Iteration: 164. Train Loss: 0.2506157159805298. Validation Accuracy: 48.69791793823242
Iteration: 165. Train Loss: 0.2498505711555481. Validation Accuracy: 50.26041793823242
Iteration: 166. Train Loss: 0.2500270903110504. Validation Accuracy: 49.21875
Iteration: 167. Train Loss: 0.25003501772880554. Validation Accuracy: 51.04166793823242
Iteration: 168. Train Loss: 0.250276654958725. Validation Accuracy: 47.91666793823242
Iteration: 169. Train Loss: 0.2502535581588745. Validation Accuracy: 47.65625
Iteration: 170. Train Loss: 0.24998974800109863. Validation Accuracy: 49.47916793823242
Iteration: 171. Train Loss: 0.25003471970558167. Validation Accuracy: 47.39583206176758
Iteration: 172. Train Loss: 0.2503085136413574. Validation Accuracy: 45.05208206176758
Iteration: 173. Train Loss: 0.24995939433574677. Validation Accuracy: 49.21875
Iteration: 174. Train Loss: 0.2499954253435135. Validation Accuracy: 50.52083206176758
Iteration: 175. Train Loss: 0.2502959072589874. Validation Accuracy: 53.64583206176758
Iteration: 176. Train Loss: 0.250229150056839. Validation Accuracy: 52.86458206176758
Iteration: 177. Train Loss: 0.24999994039535522. Validation Accuracy: 51.5625
Iteration: 178. Train Loss: 0.25055447220802307. Validation Accuracy: 49.47916793823242
Iteration: 179. Train Loss: 0.24980731308460236. Validation Accuracy: 46.09375
Iteration: 180. Train Loss: 0.24986469745635986. Validation Accuracy: 48.69791793823242
Iteration: 181. Train Loss: 0.24993501603603363. Validation Accuracy: 45.3125
Iteration: 182. Train Loss: 0.25000840425491333. Validation Accuracy: 51.30208206176758
Iteration: 183. Train Loss: 0.24975216388702393. Validation Accuracy: 46.875
Iteration: 184. Train Loss: 0.2503826916217804. Validation Accuracy: 51.04166793823242
Iteration: 185. Train Loss: 0.2500094175338745. Validation Accuracy: 47.39583206176758
Iteration: 186. Train Loss: 0.25026556849479675. Validation Accuracy: 53.64583206176758
Iteration: 187. Train Loss: 0.24983327090740204. Validation Accuracy: 51.5625
Iteration: 188. Train Loss: 0.25023168325424194. Validation Accuracy: 50.52083206176758
Iteration: 189. Train Loss: 0.24941454827785492. Validation Accuracy: 45.05208206176758
Iteration: 190. Train Loss: 0.2500699460506439. Validation Accuracy: 51.04166793823242
Iteration: 191. Train Loss: 0.2506021559238434. Validation Accuracy: 48.4375
Iteration: 192. Train Loss: 0.25030621886253357. Validation Accuracy: 53.90625
Iteration: 193. Train Loss: 0.25066468119621277. Validation Accuracy: 50.52083206176758
Iteration: 194. Train Loss: 0.2499849945306778. Validation Accuracy: 50.0
Iteration: 195. Train Loss: 0.25053441524505615. Validation Accuracy: 50.0
Iteration: 196. Train Loss: 0.25057801604270935. Validation Accuracy: 45.3125
Iteration: 197. Train Loss: 0.25010502338409424. Validation Accuracy: 50.26041793823242
Iteration: 198. Train Loss: 0.25019729137420654. Validation Accuracy: 49.47916793823242
Iteration: 199. Train Loss: 0.25006648898124695. Validation Accuracy: 47.91666793823242
Iteration: 200. Train Loss: 0.25070878863334656. Validation Accuracy: 50.26041793823242
Iteration: 201. Train Loss: 0.24996356666088104. Validation Accuracy: 49.21875
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Iteration: 4836. Train Loss: 0.1940428614616394. Validation Accuracy: 66.66666412353516
Iteration: 4837. Train Loss: 0.18951267004013062. Validation Accuracy: 65.625
Iteration: 4838. Train Loss: 0.19185858964920044. Validation Accuracy: 66.66666412353516
Iteration: 4839. Train Loss: 0.19499598443508148. Validation Accuracy: 61.97916793823242
Iteration: 4840. Train Loss: 0.17825354635715485. Validation Accuracy: 67.1875
Iteration: 4841. Train Loss: 0.21268600225448608. Validation Accuracy: 69.79166412353516
Iteration: 4842. Train Loss: 0.20194987952709198. Validation Accuracy: 66.14583587646484
Iteration: 4843. Train Loss: 0.19280189275741577. Validation Accuracy: 66.92708587646484
Iteration: 4844. Train Loss: 0.19469745457172394. Validation Accuracy: 62.5
Iteration: 4845. Train Loss: 0.2025567889213562. Validation Accuracy: 67.96875
Iteration: 4846. Train Loss: 0.20862215757369995. Validation Accuracy: 67.1875
Iteration: 4847. Train Loss: 0.20312188565731049. Validation Accuracy: 64.58333587646484
Iteration: 4848. Train Loss: 0.20010988414287567. Validation Accuracy: 66.66666412353516
Iteration: 4849. Train Loss: 0.1947440356016159. Validation Accuracy: 67.96875
Iteration: 4850. Train Loss: 0.1940569132566452. Validation Accuracy: 67.1875
Iteration: 4851. Train Loss: 0.19637452065944672. Validation Accuracy: 64.84375
Iteration: 4852. Train Loss: 0.19436897337436676. Validation Accuracy: 71.61458587646484
Iteration: 4853. Train Loss: 0.19596731662750244. Validation Accuracy: 64.32291412353516
Iteration: 4854. Train Loss: 0.2000872939825058. Validation Accuracy: 64.32291412353516
Iteration: 4855. Train Loss: 0.1926477998495102. Validation Accuracy: 63.80208206176758
Iteration: 4856. Train Loss: 0.1928693801164627. Validation Accuracy: 69.79166412353516
Iteration: 4857. Train Loss: 0.2004370540380478. Validation Accuracy: 70.83333587646484
Iteration: 4858. Train Loss: 0.19240260124206543. Validation Accuracy: 69.79166412353516
Iteration: 4859. Train Loss: 0.20266008377075195. Validation Accuracy: 61.45833206176758
Iteration: 4860. Train Loss: 0.20294658839702606. Validation Accuracy: 71.09375
Iteration: 4861. Train Loss: 0.18827563524246216. Validation Accuracy: 63.02083206176758
Iteration: 4862. Train Loss: 0.18672752380371094. Validation Accuracy: 67.1875
Iteration: 4863. Train Loss: 0.18746192753314972. Validation Accuracy: 67.1875
Iteration: 4864. Train Loss: 0.19212205708026886. Validation Accuracy: 67.70833587646484
Iteration: 4865. Train Loss: 0.19335532188415527. Validation Accuracy: 66.40625
Iteration: 4866. Train Loss: 0.1845601350069046. Validation Accuracy: 64.32291412353516
Iteration: 4867. Train Loss: 0.1806277483701706. Validation Accuracy: 66.14583587646484
Iteration: 4868. Train Loss: 0.19695723056793213. Validation Accuracy: 67.44791412353516
Iteration: 4869. Train Loss: 0.19471122324466705. Validation Accuracy: 63.54166793823242
Iteration: 4870. Train Loss: 0.190332293510437. Validation Accuracy: 68.48958587646484
Iteration: 4871. Train Loss: 0.20257514715194702. Validation Accuracy: 66.40625
Iteration: 4872. Train Loss: 0.19155184924602509. Validation Accuracy: 64.58333587646484
Iteration: 4873. Train Loss: 0.197631374001503. Validation Accuracy: 65.10416412353516
Iteration: 4874. Train Loss: 0.1819118857383728. Validation Accuracy: 69.27083587646484
Iteration: 4875. Train Loss: 0.19609718024730682. Validation Accuracy: 64.58333587646484
Iteration: 4876. Train Loss: 0.19385184347629547. Validation Accuracy: 72.39583587646484
Iteration: 4877. Train Loss: 0.19001327455043793. Validation Accuracy: 70.05208587646484
Iteration: 4878. Train Loss: 0.196939155459404. Validation Accuracy: 70.05208587646484
Iteration: 4879. Train Loss: 0.20563264191150665. Validation Accuracy: 66.14583587646484
Iteration: 4880. Train Loss: 0.1926628202199936. Validation Accuracy: 70.05208587646484
Iteration: 4881. Train Loss: 0.19627094268798828. Validation Accuracy: 64.84375
Iteration: 4882. Train Loss: 0.20048405230045319. Validation Accuracy: 65.10416412353516
Iteration: 4883. Train Loss: 0.1927025318145752. Validation Accuracy: 71.09375
Iteration: 4884. Train Loss: 0.19806893169879913. Validation Accuracy: 69.01041412353516
Iteration: 4885. Train Loss: 0.1945805549621582. Validation Accuracy: 71.09375
Iteration: 4886. Train Loss: 0.1896645575761795. Validation Accuracy: 61.19791793823242
Iteration: 4887. Train Loss: 0.1884670853614807. Validation Accuracy: 64.32291412353516
Iteration: 4888. Train Loss: 0.19416220486164093. Validation Accuracy: 61.19791793823242
Iteration: 4889. Train Loss: 0.20044320821762085. Validation Accuracy: 66.66666412353516
Iteration: 4890. Train Loss: 0.19246451556682587. Validation Accuracy: 67.1875
Iteration: 4891. Train Loss: 0.20126892626285553. Validation Accuracy: 66.66666412353516
Iteration: 4892. Train Loss: 0.1924244910478592. Validation Accuracy: 69.79166412353516
Iteration: 4893. Train Loss: 0.1894255429506302. Validation Accuracy: 69.53125
Iteration: 4894. Train Loss: 0.19652442634105682. Validation Accuracy: 65.36458587646484
Iteration: 4895. Train Loss: 0.20191234350204468. Validation Accuracy: 64.84375
Iteration: 4896. Train Loss: 0.1938169002532959. Validation Accuracy: 58.85416793823242
Iteration: 4897. Train Loss: 0.19009478390216827. Validation Accuracy: 65.10416412353516
Iteration: 4898. Train Loss: 0.20095346868038177. Validation Accuracy: 69.27083587646484
Iteration: 4899. Train Loss: 0.19224637746810913. Validation Accuracy: 68.22916412353516
Iteration: 4900. Train Loss: 0.18843387067317963. Validation Accuracy: 64.58333587646484
Iteration: 4901. Train Loss: 0.18989841639995575. Validation Accuracy: 66.66666412353516
Iteration: 4902. Train Loss: 0.20067648589611053. Validation Accuracy: 62.5
Iteration: 4903. Train Loss: 0.18005035817623138. Validation Accuracy: 69.01041412353516
Iteration: 4904. Train Loss: 0.1935083121061325. Validation Accuracy: 65.625
Iteration: 4905. Train Loss: 0.19484198093414307. Validation Accuracy: 66.40625
Iteration: 4906. Train Loss: 0.18886959552764893. Validation Accuracy: 64.32291412353516
Iteration: 4907. Train Loss: 0.1909494251012802. Validation Accuracy: 70.05208587646484
Iteration: 4908. Train Loss: 0.19000858068466187. Validation Accuracy: 69.27083587646484
Iteration: 4909. Train Loss: 0.20199136435985565. Validation Accuracy: 71.35416412353516
Iteration: 4910. Train Loss: 0.20504619181156158. Validation Accuracy: 64.32291412353516
Iteration: 4911. Train Loss: 0.20356673002243042. Validation Accuracy: 66.14583587646484
Iteration: 4912. Train Loss: 0.19021707773208618. Validation Accuracy: 67.44791412353516
Iteration: 4913. Train Loss: 0.1859235316514969. Validation Accuracy: 65.625
Iteration: 4914. Train Loss: 0.18678508698940277. Validation Accuracy: 74.21875
Iteration: 4915. Train Loss: 0.18544882535934448. Validation Accuracy: 74.21875
Iteration: 4916. Train Loss: 0.19942916929721832. Validation Accuracy: 67.44791412353516
Iteration: 4917. Train Loss: 0.1914120763540268. Validation Accuracy: 69.27083587646484
Iteration: 4918. Train Loss: 0.20584367215633392. Validation Accuracy: 65.10416412353516
Iteration: 4919. Train Loss: 0.20954932272434235. Validation Accuracy: 63.28125
Iteration: 4920. Train Loss: 0.18108312785625458. Validation Accuracy: 63.28125
Iteration: 4921. Train Loss: 0.18684320151805878. Validation Accuracy: 69.01041412353516
Iteration: 4922. Train Loss: 0.1974526196718216. Validation Accuracy: 64.58333587646484
Iteration: 4923. Train Loss: 0.18828712403774261. Validation Accuracy: 62.5
Iteration: 4924. Train Loss: 0.19696901738643646. Validation Accuracy: 61.45833206176758
Iteration: 4925. Train Loss: 0.1969279646873474. Validation Accuracy: 63.54166793823242
Iteration: 4926. Train Loss: 0.19918902218341827. Validation Accuracy: 68.22916412353516
Iteration: 4927. Train Loss: 0.1902596354484558. Validation Accuracy: 64.0625
Iteration: 4928. Train Loss: 0.19642651081085205. Validation Accuracy: 61.19791793823242
Iteration: 4929. Train Loss: 0.188223198056221. Validation Accuracy: 67.1875
Iteration: 4930. Train Loss: 0.18111485242843628. Validation Accuracy: 59.375
Iteration: 4931. Train Loss: 0.1826775223016739. Validation Accuracy: 68.22916412353516
Iteration: 4932. Train Loss: 0.18713168799877167. Validation Accuracy: 66.14583587646484
Iteration: 4933. Train Loss: 0.18061339855194092. Validation Accuracy: 67.1875
Iteration: 4934. Train Loss: 0.19063664972782135. Validation Accuracy: 65.625
Iteration: 4935. Train Loss: 0.18273212015628815. Validation Accuracy: 68.22916412353516
Iteration: 4936. Train Loss: 0.18863362073898315. Validation Accuracy: 70.57291412353516
Iteration: 4937. Train Loss: 0.19200263917446136. Validation Accuracy: 65.36458587646484
Iteration: 4938. Train Loss: 0.19222062826156616. Validation Accuracy: 68.48958587646484
Iteration: 4939. Train Loss: 0.20081989467144012. Validation Accuracy: 63.80208206176758
Iteration: 4940. Train Loss: 0.19975493848323822. Validation Accuracy: 68.48958587646484
Iteration: 4941. Train Loss: 0.18811921775341034. Validation Accuracy: 64.58333587646484
Iteration: 4942. Train Loss: 0.21169614791870117. Validation Accuracy: 68.22916412353516
Iteration: 4943. Train Loss: 0.18864686787128448. Validation Accuracy: 61.97916793823242
Iteration: 4944. Train Loss: 0.184439554810524. Validation Accuracy: 66.92708587646484
Iteration: 4945. Train Loss: 0.2024592161178589. Validation Accuracy: 67.96875
Iteration: 4946. Train Loss: 0.19073355197906494. Validation Accuracy: 63.54166793823242
Iteration: 4947. Train Loss: 0.18346279859542847. Validation Accuracy: 73.69791412353516
Iteration: 4948. Train Loss: 0.1804952770471573. Validation Accuracy: 65.10416412353516
Iteration: 4949. Train Loss: 0.19564735889434814. Validation Accuracy: 70.05208587646484
Iteration: 4950. Train Loss: 0.18928658962249756. Validation Accuracy: 66.66666412353516
Iteration: 4951. Train Loss: 0.19340316951274872. Validation Accuracy: 69.27083587646484
Iteration: 4952. Train Loss: 0.2025730460882187. Validation Accuracy: 68.48958587646484
Iteration: 4953. Train Loss: 0.19317996501922607. Validation Accuracy: 67.1875
Iteration: 4954. Train Loss: 0.19419431686401367. Validation Accuracy: 64.58333587646484
Iteration: 4955. Train Loss: 0.18423950672149658. Validation Accuracy: 63.28125
Iteration: 4956. Train Loss: 0.19419829547405243. Validation Accuracy: 68.48958587646484
Iteration: 4957. Train Loss: 0.18285147845745087. Validation Accuracy: 70.05208587646484
Iteration: 4958. Train Loss: 0.19686578214168549. Validation Accuracy: 67.70833587646484
Iteration: 4959. Train Loss: 0.19160766899585724. Validation Accuracy: 73.4375
Iteration: 4960. Train Loss: 0.19803576171398163. Validation Accuracy: 69.79166412353516
Iteration: 4961. Train Loss: 0.19647429883480072. Validation Accuracy: 70.83333587646484
Iteration: 4962. Train Loss: 0.20642435550689697. Validation Accuracy: 67.96875
Iteration: 4963. Train Loss: 0.20975661277770996. Validation Accuracy: 66.66666412353516
Iteration: 4964. Train Loss: 0.19085918366909027. Validation Accuracy: 67.1875
Iteration: 4965. Train Loss: 0.1943119913339615. Validation Accuracy: 64.0625
Iteration: 4966. Train Loss: 0.20442543923854828. Validation Accuracy: 73.95833587646484
Iteration: 4967. Train Loss: 0.20246262848377228. Validation Accuracy: 68.22916412353516
Iteration: 4968. Train Loss: 0.2024661749601364. Validation Accuracy: 71.875
Iteration: 4969. Train Loss: 0.19907192885875702. Validation Accuracy: 73.4375
Iteration: 4970. Train Loss: 0.2003300040960312. Validation Accuracy: 72.39583587646484
Iteration: 4971. Train Loss: 0.18322743475437164. Validation Accuracy: 66.92708587646484
Iteration: 4972. Train Loss: 0.1929028034210205. Validation Accuracy: 66.40625
Iteration: 4973. Train Loss: 0.1866479367017746. Validation Accuracy: 60.67708206176758
Iteration: 4974. Train Loss: 0.19841724634170532. Validation Accuracy: 62.5
Iteration: 4975. Train Loss: 0.20062313973903656. Validation Accuracy: 66.40625
Iteration: 4976. Train Loss: 0.18799211084842682. Validation Accuracy: 72.65625
Iteration: 4977. Train Loss: 0.19756031036376953. Validation Accuracy: 69.01041412353516
Iteration: 4978. Train Loss: 0.19212646782398224. Validation Accuracy: 63.80208206176758
Iteration: 4979. Train Loss: 0.18977975845336914. Validation Accuracy: 69.79166412353516
Iteration: 4980. Train Loss: 0.1998312920331955. Validation Accuracy: 71.35416412353516
Iteration: 4981. Train Loss: 0.19873517751693726. Validation Accuracy: 62.76041793823242
Iteration: 4982. Train Loss: 0.19796814024448395. Validation Accuracy: 72.39583587646484
Iteration: 4983. Train Loss: 0.18794022500514984. Validation Accuracy: 60.9375
Iteration: 4984. Train Loss: 0.19519950449466705. Validation Accuracy: 71.61458587646484
Iteration: 4985. Train Loss: 0.2007991075515747. Validation Accuracy: 67.70833587646484
Iteration: 4986. Train Loss: 0.1996324509382248. Validation Accuracy: 64.58333587646484
Iteration: 4987. Train Loss: 0.19567649066448212. Validation Accuracy: 62.5
Iteration: 4988. Train Loss: 0.21612967550754547. Validation Accuracy: 68.22916412353516
Iteration: 4989. Train Loss: 0.19064109027385712. Validation Accuracy: 69.01041412353516
Iteration: 4990. Train Loss: 0.1812659054994583. Validation Accuracy: 65.36458587646484
Iteration: 4991. Train Loss: 0.19765253365039825. Validation Accuracy: 62.76041793823242
Iteration: 4992. Train Loss: 0.20731250941753387. Validation Accuracy: 67.44791412353516
Iteration: 4993. Train Loss: 0.19352005422115326. Validation Accuracy: 64.32291412353516
Iteration: 4994. Train Loss: 0.19639764726161957. Validation Accuracy: 62.76041793823242
Iteration: 4995. Train Loss: 0.18781954050064087. Validation Accuracy: 64.84375
Iteration: 4996. Train Loss: 0.18732939660549164. Validation Accuracy: 71.35416412353516
Iteration: 4997. Train Loss: 0.20440174639225006. Validation Accuracy: 66.40625
Iteration: 4998. Train Loss: 0.1933826059103012. Validation Accuracy: 65.10416412353516
Iteration: 4999. Train Loss: 0.2071862667798996. Validation Accuracy: 68.22916412353516
Finished Training
Learning curves¶
In [ ]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [ ]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Accuracy vs L¶
In [ ]:
acc = []
for L in range(1, 21):
inputs, labels = get_batch(100, L)
outputs = model(inputs)
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
acc.append(accuracy_score(labels.flatten().numpy(), predicted.numpy()))
In [ ]:
# Plot the val accuracy
plt.plot(range(1, 21), acc)
plt.ylabel('Accuracy')
plt.xlabel('Sequence length - L')
plt.title('Accuracy vs L plot')
plt.show()
We trained the network with hidden state size of 5 but with MSE loss. The average accuracy is 68% compared to 74% when trained with cross entropy loss (with same hidden state size)
For all the above experiments, we have fixed L = 5 during training. We have provided the average accuracy vs L plot during inference. The average accuracy falls as L increases. We now train networks with L = 3 and L = 10 with hidden state size = 5
L = 3¶
In [39]:
INPUT_DIM = 2
HIDDEN_DIM = 5
OUTPUT_DIM = 2
NUM_LAYERS = 5
LR = 0.001
K = 64
L = 3
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [40]:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [41]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for iteration in range(5000):
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs.view(-1, 2), labels)
loss.backward()
optimizer.step()
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# Forward pass only to get logits/output
outputs = model(inputs)
val_cost = criterion(outputs.view(-1, 2), labels)
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(iteration, loss.item(), accuracy))
print('Finished Training')
Streaming output truncated to the last 5000 lines.
Iteration: 1. Train Loss: 0.7131499648094177. Validation Accuracy: 53.125
Iteration: 2. Train Loss: 0.715872585773468. Validation Accuracy: 50.390625
Iteration: 3. Train Loss: 0.7282714247703552. Validation Accuracy: 49.21875
Iteration: 4. Train Loss: 0.7169039845466614. Validation Accuracy: 46.09375
Iteration: 5. Train Loss: 0.7142415642738342. Validation Accuracy: 52.34375
Iteration: 6. Train Loss: 0.7126679420471191. Validation Accuracy: 51.5625
Iteration: 7. Train Loss: 0.7204973697662354. Validation Accuracy: 49.609375
Iteration: 8. Train Loss: 0.7224205732345581. Validation Accuracy: 51.171875
Iteration: 9. Train Loss: 0.7138694524765015. Validation Accuracy: 46.484375
Iteration: 10. Train Loss: 0.7069961428642273. Validation Accuracy: 51.171875
Iteration: 11. Train Loss: 0.7120761275291443. Validation Accuracy: 46.09375
Iteration: 12. Train Loss: 0.7107518315315247. Validation Accuracy: 52.34375
Iteration: 13. Train Loss: 0.7107962369918823. Validation Accuracy: 52.34375
Iteration: 14. Train Loss: 0.7070357203483582. Validation Accuracy: 51.171875
Iteration: 15. Train Loss: 0.7089399695396423. Validation Accuracy: 49.609375
Iteration: 16. Train Loss: 0.7016772031784058. Validation Accuracy: 49.609375
Iteration: 17. Train Loss: 0.7282194495201111. Validation Accuracy: 47.65625
Iteration: 18. Train Loss: 0.7000235319137573. Validation Accuracy: 50.78125
Iteration: 19. Train Loss: 0.7147543430328369. Validation Accuracy: 50.78125
Iteration: 20. Train Loss: 0.700869083404541. Validation Accuracy: 52.734375
Iteration: 21. Train Loss: 0.7188736796379089. Validation Accuracy: 50.78125
Iteration: 22. Train Loss: 0.7039015293121338. Validation Accuracy: 52.734375
Iteration: 23. Train Loss: 0.6916570663452148. Validation Accuracy: 53.125
Iteration: 24. Train Loss: 0.7028393149375916. Validation Accuracy: 49.21875
Iteration: 25. Train Loss: 0.7121126055717468. Validation Accuracy: 49.609375
Iteration: 26. Train Loss: 0.7033010125160217. Validation Accuracy: 49.609375
Iteration: 27. Train Loss: 0.7088416814804077. Validation Accuracy: 47.65625
Iteration: 28. Train Loss: 0.7065213322639465. Validation Accuracy: 48.4375
Iteration: 29. Train Loss: 0.7176598906517029. Validation Accuracy: 54.296875
Iteration: 30. Train Loss: 0.7117795944213867. Validation Accuracy: 50.390625
Iteration: 31. Train Loss: 0.699380099773407. Validation Accuracy: 51.171875
Iteration: 32. Train Loss: 0.7046561241149902. Validation Accuracy: 50.390625
Iteration: 33. Train Loss: 0.7148360013961792. Validation Accuracy: 52.34375
Iteration: 34. Train Loss: 0.704084038734436. Validation Accuracy: 48.828125
Iteration: 35. Train Loss: 0.7021930813789368. Validation Accuracy: 50.0
Iteration: 36. Train Loss: 0.7114109992980957. Validation Accuracy: 50.78125
Iteration: 37. Train Loss: 0.7087361216545105. Validation Accuracy: 50.78125
Iteration: 38. Train Loss: 0.7034659385681152. Validation Accuracy: 51.5625
Iteration: 39. Train Loss: 0.7052974104881287. Validation Accuracy: 43.75
Iteration: 40. Train Loss: 0.7051588892936707. Validation Accuracy: 50.0
Iteration: 41. Train Loss: 0.7043156027793884. Validation Accuracy: 51.953125
Iteration: 42. Train Loss: 0.7042130827903748. Validation Accuracy: 44.921875
Iteration: 43. Train Loss: 0.703845202922821. Validation Accuracy: 49.609375
Iteration: 44. Train Loss: 0.7001587152481079. Validation Accuracy: 51.171875
Iteration: 45. Train Loss: 0.7033577561378479. Validation Accuracy: 51.953125
Iteration: 46. Train Loss: 0.6993040442466736. Validation Accuracy: 44.140625
Iteration: 47. Train Loss: 0.7124540209770203. Validation Accuracy: 50.0
Iteration: 48. Train Loss: 0.7048669457435608. Validation Accuracy: 47.65625
Iteration: 49. Train Loss: 0.7043507695198059. Validation Accuracy: 51.5625
Iteration: 50. Train Loss: 0.7016664743423462. Validation Accuracy: 49.21875
Iteration: 51. Train Loss: 0.7061558961868286. Validation Accuracy: 51.953125
Iteration: 52. Train Loss: 0.6975029110908508. Validation Accuracy: 50.78125
Iteration: 53. Train Loss: 0.7047356367111206. Validation Accuracy: 51.171875
Iteration: 54. Train Loss: 0.7019224166870117. Validation Accuracy: 51.5625
Iteration: 55. Train Loss: 0.6924163699150085. Validation Accuracy: 53.90625
Iteration: 56. Train Loss: 0.7000024318695068. Validation Accuracy: 50.0
Iteration: 57. Train Loss: 0.70196932554245. Validation Accuracy: 50.78125
Iteration: 58. Train Loss: 0.70391845703125. Validation Accuracy: 50.78125
Iteration: 59. Train Loss: 0.6988230347633362. Validation Accuracy: 48.828125
Iteration: 60. Train Loss: 0.6996270418167114. Validation Accuracy: 50.78125
Iteration: 61. Train Loss: 0.6953491568565369. Validation Accuracy: 51.5625
Iteration: 62. Train Loss: 0.7054005861282349. Validation Accuracy: 50.0
Iteration: 63. Train Loss: 0.7032281160354614. Validation Accuracy: 48.4375
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Iteration: 4839. Train Loss: 0.45141103863716125. Validation Accuracy: 75.0
Iteration: 4840. Train Loss: 0.44732409715652466. Validation Accuracy: 71.09375
Iteration: 4841. Train Loss: 0.45045205950737. Validation Accuracy: 78.515625
Iteration: 4842. Train Loss: 0.4563715159893036. Validation Accuracy: 70.3125
Iteration: 4843. Train Loss: 0.4727744460105896. Validation Accuracy: 76.171875
Iteration: 4844. Train Loss: 0.44736453890800476. Validation Accuracy: 75.0
Iteration: 4845. Train Loss: 0.4279681146144867. Validation Accuracy: 74.21875
Iteration: 4846. Train Loss: 0.45151811838150024. Validation Accuracy: 86.328125
Iteration: 4847. Train Loss: 0.41614365577697754. Validation Accuracy: 76.953125
Iteration: 4848. Train Loss: 0.4481361210346222. Validation Accuracy: 69.921875
Iteration: 4849. Train Loss: 0.4650607407093048. Validation Accuracy: 75.0
Iteration: 4850. Train Loss: 0.42044782638549805. Validation Accuracy: 72.65625
Iteration: 4851. Train Loss: 0.4432206153869629. Validation Accuracy: 75.78125
Iteration: 4852. Train Loss: 0.4665335714817047. Validation Accuracy: 76.171875
Iteration: 4853. Train Loss: 0.3924131393432617. Validation Accuracy: 73.046875
Iteration: 4854. Train Loss: 0.4685741364955902. Validation Accuracy: 73.4375
Iteration: 4855. Train Loss: 0.4639829695224762. Validation Accuracy: 68.75
Iteration: 4856. Train Loss: 0.4851798415184021. Validation Accuracy: 75.0
Iteration: 4857. Train Loss: 0.5259497761726379. Validation Accuracy: 79.296875
Iteration: 4858. Train Loss: 0.42140355706214905. Validation Accuracy: 79.296875
Iteration: 4859. Train Loss: 0.4461316168308258. Validation Accuracy: 70.703125
Iteration: 4860. Train Loss: 0.4573310613632202. Validation Accuracy: 71.484375
Iteration: 4861. Train Loss: 0.44990071654319763. Validation Accuracy: 78.90625
Iteration: 4862. Train Loss: 0.4823454022407532. Validation Accuracy: 75.78125
Iteration: 4863. Train Loss: 0.4361247718334198. Validation Accuracy: 72.265625
Iteration: 4864. Train Loss: 0.4635120630264282. Validation Accuracy: 73.4375
Iteration: 4865. Train Loss: 0.4491608142852783. Validation Accuracy: 79.296875
Iteration: 4866. Train Loss: 0.44940897822380066. Validation Accuracy: 77.34375
Iteration: 4867. Train Loss: 0.44947153329849243. Validation Accuracy: 74.609375
Iteration: 4868. Train Loss: 0.4840618073940277. Validation Accuracy: 72.265625
Iteration: 4869. Train Loss: 0.43495088815689087. Validation Accuracy: 76.5625
Iteration: 4870. Train Loss: 0.4555383026599884. Validation Accuracy: 78.515625
Iteration: 4871. Train Loss: 0.49614599347114563. Validation Accuracy: 73.828125
Iteration: 4872. Train Loss: 0.4382103383541107. Validation Accuracy: 77.34375
Iteration: 4873. Train Loss: 0.4464016556739807. Validation Accuracy: 71.09375
Iteration: 4874. Train Loss: 0.48052769899368286. Validation Accuracy: 73.828125
Iteration: 4875. Train Loss: 0.468860000371933. Validation Accuracy: 76.953125
Iteration: 4876. Train Loss: 0.4535815119743347. Validation Accuracy: 80.859375
Iteration: 4877. Train Loss: 0.42138686776161194. Validation Accuracy: 76.171875
Iteration: 4878. Train Loss: 0.45599424839019775. Validation Accuracy: 79.296875
Iteration: 4879. Train Loss: 0.4583766460418701. Validation Accuracy: 75.78125
Iteration: 4880. Train Loss: 0.4307421147823334. Validation Accuracy: 77.34375
Iteration: 4881. Train Loss: 0.43398457765579224. Validation Accuracy: 78.125
Iteration: 4882. Train Loss: 0.48987841606140137. Validation Accuracy: 76.953125
Iteration: 4883. Train Loss: 0.4791845381259918. Validation Accuracy: 73.4375
Iteration: 4884. Train Loss: 0.4663532078266144. Validation Accuracy: 80.078125
Iteration: 4885. Train Loss: 0.49141767621040344. Validation Accuracy: 72.265625
Iteration: 4886. Train Loss: 0.47071078419685364. Validation Accuracy: 76.5625
Iteration: 4887. Train Loss: 0.4313473701477051. Validation Accuracy: 76.5625
Iteration: 4888. Train Loss: 0.4358058571815491. Validation Accuracy: 77.734375
Iteration: 4889. Train Loss: 0.4323984682559967. Validation Accuracy: 76.5625
Iteration: 4890. Train Loss: 0.45993730425834656. Validation Accuracy: 75.78125
Iteration: 4891. Train Loss: 0.460814893245697. Validation Accuracy: 77.734375
Iteration: 4892. Train Loss: 0.4808517396450043. Validation Accuracy: 73.4375
Iteration: 4893. Train Loss: 0.4440068304538727. Validation Accuracy: 72.65625
Iteration: 4894. Train Loss: 0.4330042600631714. Validation Accuracy: 78.515625
Iteration: 4895. Train Loss: 0.45247769355773926. Validation Accuracy: 76.5625
Iteration: 4896. Train Loss: 0.42377373576164246. Validation Accuracy: 76.171875
Iteration: 4897. Train Loss: 0.4632677435874939. Validation Accuracy: 75.78125
Iteration: 4898. Train Loss: 0.45976442098617554. Validation Accuracy: 80.46875
Iteration: 4899. Train Loss: 0.4633789658546448. Validation Accuracy: 73.4375
Iteration: 4900. Train Loss: 0.46280908584594727. Validation Accuracy: 69.140625
Iteration: 4901. Train Loss: 0.4653506278991699. Validation Accuracy: 80.859375
Iteration: 4902. Train Loss: 0.46467503905296326. Validation Accuracy: 72.65625
Iteration: 4903. Train Loss: 0.47942671179771423. Validation Accuracy: 76.5625
Iteration: 4904. Train Loss: 0.43668603897094727. Validation Accuracy: 80.078125
Iteration: 4905. Train Loss: 0.4676390588283539. Validation Accuracy: 71.09375
Iteration: 4906. Train Loss: 0.4401049315929413. Validation Accuracy: 77.34375
Iteration: 4907. Train Loss: 0.43670961260795593. Validation Accuracy: 77.734375
Iteration: 4908. Train Loss: 0.47105318307876587. Validation Accuracy: 74.609375
Iteration: 4909. Train Loss: 0.49033433198928833. Validation Accuracy: 80.859375
Iteration: 4910. Train Loss: 0.4744049906730652. Validation Accuracy: 74.609375
Iteration: 4911. Train Loss: 0.476464182138443. Validation Accuracy: 75.78125
Iteration: 4912. Train Loss: 0.4571281969547272. Validation Accuracy: 66.40625
Iteration: 4913. Train Loss: 0.41945603489875793. Validation Accuracy: 74.21875
Iteration: 4914. Train Loss: 0.4270021319389343. Validation Accuracy: 76.5625
Iteration: 4915. Train Loss: 0.45506757497787476. Validation Accuracy: 73.828125
Iteration: 4916. Train Loss: 0.42895635962486267. Validation Accuracy: 77.734375
Iteration: 4917. Train Loss: 0.47662368416786194. Validation Accuracy: 71.09375
Iteration: 4918. Train Loss: 0.44102758169174194. Validation Accuracy: 76.5625
Iteration: 4919. Train Loss: 0.42352017760276794. Validation Accuracy: 82.421875
Iteration: 4920. Train Loss: 0.45388802886009216. Validation Accuracy: 71.484375
Iteration: 4921. Train Loss: 0.39192262291908264. Validation Accuracy: 74.609375
Iteration: 4922. Train Loss: 0.48053741455078125. Validation Accuracy: 73.828125
Iteration: 4923. Train Loss: 0.49443161487579346. Validation Accuracy: 71.484375
Iteration: 4924. Train Loss: 0.48452091217041016. Validation Accuracy: 80.46875
Iteration: 4925. Train Loss: 0.46866557002067566. Validation Accuracy: 82.421875
Iteration: 4926. Train Loss: 0.4777669608592987. Validation Accuracy: 74.609375
Iteration: 4927. Train Loss: 0.44666263461112976. Validation Accuracy: 75.78125
Iteration: 4928. Train Loss: 0.4378446638584137. Validation Accuracy: 78.125
Iteration: 4929. Train Loss: 0.45171836018562317. Validation Accuracy: 78.515625
Iteration: 4930. Train Loss: 0.476362019777298. Validation Accuracy: 71.875
Iteration: 4931. Train Loss: 0.426143616437912. Validation Accuracy: 76.5625
Iteration: 4932. Train Loss: 0.46303457021713257. Validation Accuracy: 76.5625
Iteration: 4933. Train Loss: 0.4473755955696106. Validation Accuracy: 75.0
Iteration: 4934. Train Loss: 0.44808879494667053. Validation Accuracy: 69.921875
Iteration: 4935. Train Loss: 0.47894319891929626. Validation Accuracy: 73.828125
Iteration: 4936. Train Loss: 0.45339488983154297. Validation Accuracy: 75.78125
Iteration: 4937. Train Loss: 0.5100417733192444. Validation Accuracy: 79.296875
Iteration: 4938. Train Loss: 0.45080915093421936. Validation Accuracy: 75.0
Iteration: 4939. Train Loss: 0.45361244678497314. Validation Accuracy: 82.8125
Iteration: 4940. Train Loss: 0.4432576596736908. Validation Accuracy: 73.828125
Iteration: 4941. Train Loss: 0.420180082321167. Validation Accuracy: 73.828125
Iteration: 4942. Train Loss: 0.4574928879737854. Validation Accuracy: 74.609375
Iteration: 4943. Train Loss: 0.43808338046073914. Validation Accuracy: 66.40625
Iteration: 4944. Train Loss: 0.441689670085907. Validation Accuracy: 77.34375
Iteration: 4945. Train Loss: 0.501479983329773. Validation Accuracy: 73.046875
Iteration: 4946. Train Loss: 0.4749903082847595. Validation Accuracy: 69.921875
Iteration: 4947. Train Loss: 0.43610841035842896. Validation Accuracy: 79.296875
Iteration: 4948. Train Loss: 0.446900874376297. Validation Accuracy: 77.34375
Iteration: 4949. Train Loss: 0.49185365438461304. Validation Accuracy: 70.3125
Iteration: 4950. Train Loss: 0.42021632194519043. Validation Accuracy: 78.125
Iteration: 4951. Train Loss: 0.48184770345687866. Validation Accuracy: 80.078125
Iteration: 4952. Train Loss: 0.4663375914096832. Validation Accuracy: 78.90625
Iteration: 4953. Train Loss: 0.4483359158039093. Validation Accuracy: 73.4375
Iteration: 4954. Train Loss: 0.43904203176498413. Validation Accuracy: 80.46875
Iteration: 4955. Train Loss: 0.46470409631729126. Validation Accuracy: 76.171875
Iteration: 4956. Train Loss: 0.45193517208099365. Validation Accuracy: 71.875
Iteration: 4957. Train Loss: 0.45596200227737427. Validation Accuracy: 74.609375
Iteration: 4958. Train Loss: 0.4751560688018799. Validation Accuracy: 73.828125
Iteration: 4959. Train Loss: 0.41167402267456055. Validation Accuracy: 78.90625
Iteration: 4960. Train Loss: 0.48343443870544434. Validation Accuracy: 77.34375
Iteration: 4961. Train Loss: 0.44209685921669006. Validation Accuracy: 78.515625
Iteration: 4962. Train Loss: 0.4680541157722473. Validation Accuracy: 75.0
Iteration: 4963. Train Loss: 0.4696447551250458. Validation Accuracy: 75.390625
Iteration: 4964. Train Loss: 0.4168887436389923. Validation Accuracy: 76.171875
Iteration: 4965. Train Loss: 0.4640756845474243. Validation Accuracy: 73.828125
Iteration: 4966. Train Loss: 0.4458979368209839. Validation Accuracy: 69.921875
Iteration: 4967. Train Loss: 0.4440409541130066. Validation Accuracy: 80.859375
Iteration: 4968. Train Loss: 0.4630639851093292. Validation Accuracy: 71.484375
Iteration: 4969. Train Loss: 0.45947709679603577. Validation Accuracy: 65.625
Iteration: 4970. Train Loss: 0.45188090205192566. Validation Accuracy: 78.125
Iteration: 4971. Train Loss: 0.5328913927078247. Validation Accuracy: 83.59375
Iteration: 4972. Train Loss: 0.45812779664993286. Validation Accuracy: 69.140625
Iteration: 4973. Train Loss: 0.43644845485687256. Validation Accuracy: 77.734375
Iteration: 4974. Train Loss: 0.4683012068271637. Validation Accuracy: 77.34375
Iteration: 4975. Train Loss: 0.45357802510261536. Validation Accuracy: 71.484375
Iteration: 4976. Train Loss: 0.47932305932044983. Validation Accuracy: 77.34375
Iteration: 4977. Train Loss: 0.4536373019218445. Validation Accuracy: 73.828125
Iteration: 4978. Train Loss: 0.41381239891052246. Validation Accuracy: 76.5625
Iteration: 4979. Train Loss: 0.4506719410419464. Validation Accuracy: 80.46875
Iteration: 4980. Train Loss: 0.49618250131607056. Validation Accuracy: 73.046875
Iteration: 4981. Train Loss: 0.4659869074821472. Validation Accuracy: 75.78125
Iteration: 4982. Train Loss: 0.45540183782577515. Validation Accuracy: 73.4375
Iteration: 4983. Train Loss: 0.47154778242111206. Validation Accuracy: 76.5625
Iteration: 4984. Train Loss: 0.46479207277297974. Validation Accuracy: 70.3125
Iteration: 4985. Train Loss: 0.4840623438358307. Validation Accuracy: 78.125
Iteration: 4986. Train Loss: 0.4418427050113678. Validation Accuracy: 79.296875
Iteration: 4987. Train Loss: 0.46310409903526306. Validation Accuracy: 76.5625
Iteration: 4988. Train Loss: 0.45848774909973145. Validation Accuracy: 72.265625
Iteration: 4989. Train Loss: 0.48758551478385925. Validation Accuracy: 74.21875
Iteration: 4990. Train Loss: 0.44374778866767883. Validation Accuracy: 72.65625
Iteration: 4991. Train Loss: 0.4785652756690979. Validation Accuracy: 72.265625
Iteration: 4992. Train Loss: 0.47321948409080505. Validation Accuracy: 75.390625
Iteration: 4993. Train Loss: 0.4430215358734131. Validation Accuracy: 74.609375
Iteration: 4994. Train Loss: 0.47992339730262756. Validation Accuracy: 68.75
Iteration: 4995. Train Loss: 0.4573988616466522. Validation Accuracy: 70.703125
Iteration: 4996. Train Loss: 0.4586816728115082. Validation Accuracy: 66.015625
Iteration: 4997. Train Loss: 0.43868178129196167. Validation Accuracy: 73.4375
Iteration: 4998. Train Loss: 0.4326058030128479. Validation Accuracy: 71.09375
Iteration: 4999. Train Loss: 0.4899095296859741. Validation Accuracy: 74.609375
Finished Training
Learning curves¶
In [42]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [43]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Accuracy vs L¶
In [44]:
acc = []
for L in range(1, 21):
inputs, labels = get_batch(100, L)
outputs = model(inputs)
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
acc.append(accuracy_score(labels.flatten().numpy(), predicted.numpy()))
In [45]:
# Plot the val accuracy
plt.plot(range(1, 21), acc)
plt.ylabel('Accuracy')
plt.xlabel('Sequence length - L')
plt.title('Accuracy vs L plot')
plt.show()
L = 10¶
In [46]:
INPUT_DIM = 2
HIDDEN_DIM = 5
OUTPUT_DIM = 2
NUM_LAYERS = 5
LR = 0.001
K = 64
L = 10
model = RNN(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, NUM_LAYERS)
In [47]:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = LR) #, momentum = 0.9)
In [48]:
train_costs = []
val_costs = []
train_acc = []
val_acc = []
for iteration in range(5000):
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs.view(-1, 2), labels)
loss.backward()
optimizer.step()
train_costs.append(loss.item())
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
train_acc.append(accuracy)
correct = 0
total = 0
inputs, labels = get_batch(K, L)
labels = labels.flatten()
# Forward pass only to get logits/output
outputs = model(inputs)
val_cost = criterion(outputs.view(-1, 2), labels)
val_costs.append(val_cost.item())
# Get predictions from the maximum value
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
val_acc.append(accuracy)
# Print Loss
print('Iteration: {}. Train Loss: {}. Validation Accuracy: {}'.format(iteration, loss.item(), accuracy))
print('Finished Training')
Streaming output truncated to the last 5000 lines.
Iteration: 1. Train Loss: 0.7009475231170654. Validation Accuracy: 48.4375
Iteration: 2. Train Loss: 0.6908105611801147. Validation Accuracy: 50.994319915771484
Iteration: 3. Train Loss: 0.6949511170387268. Validation Accuracy: 48.72159194946289
Iteration: 4. Train Loss: 0.6956255435943604. Validation Accuracy: 48.4375
Iteration: 5. Train Loss: 0.6989268660545349. Validation Accuracy: 49.85795593261719
Iteration: 6. Train Loss: 0.6936454176902771. Validation Accuracy: 50.28409194946289
Iteration: 7. Train Loss: 0.6977441310882568. Validation Accuracy: 51.42045593261719
Iteration: 8. Train Loss: 0.6951209306716919. Validation Accuracy: 50.852272033691406
Iteration: 9. Train Loss: 0.6979852318763733. Validation Accuracy: 49.5738639831543
Iteration: 10. Train Loss: 0.6940798163414001. Validation Accuracy: 51.42045593261719
Iteration: 11. Train Loss: 0.6948841214179993. Validation Accuracy: 52.272727966308594
Iteration: 12. Train Loss: 0.6962687969207764. Validation Accuracy: 53.693180084228516
Iteration: 13. Train Loss: 0.6927749514579773. Validation Accuracy: 49.289772033691406
Iteration: 14. Train Loss: 0.6955316066741943. Validation Accuracy: 48.57954406738281
Iteration: 15. Train Loss: 0.694485604763031. Validation Accuracy: 48.15340805053711
Iteration: 16. Train Loss: 0.6928957104682922. Validation Accuracy: 49.85795593261719
Iteration: 17. Train Loss: 0.6944644451141357. Validation Accuracy: 49.147727966308594
Iteration: 18. Train Loss: 0.6913265585899353. Validation Accuracy: 51.27840805053711
Iteration: 19. Train Loss: 0.6926171779632568. Validation Accuracy: 49.431819915771484
Iteration: 20. Train Loss: 0.6967878937721252. Validation Accuracy: 48.57954406738281
Iteration: 21. Train Loss: 0.6944005489349365. Validation Accuracy: 51.84659194946289
Iteration: 22. Train Loss: 0.6946078538894653. Validation Accuracy: 51.27840805053711
Iteration: 23. Train Loss: 0.6936218738555908. Validation Accuracy: 49.71590805053711
Iteration: 24. Train Loss: 0.6935262680053711. Validation Accuracy: 49.289772033691406
Iteration: 25. Train Loss: 0.6938167214393616. Validation Accuracy: 50.852272033691406
Iteration: 26. Train Loss: 0.6941156387329102. Validation Accuracy: 51.1363639831543
Iteration: 27. Train Loss: 0.6935552954673767. Validation Accuracy: 49.005680084228516
Iteration: 28. Train Loss: 0.6932844519615173. Validation Accuracy: 50.710227966308594
Iteration: 29. Train Loss: 0.6933285593986511. Validation Accuracy: 52.130680084228516
Iteration: 30. Train Loss: 0.6932311058044434. Validation Accuracy: 49.71590805053711
Iteration: 31. Train Loss: 0.692737340927124. Validation Accuracy: 48.57954406738281
Iteration: 32. Train Loss: 0.6937153339385986. Validation Accuracy: 50.0
Iteration: 33. Train Loss: 0.692942202091217. Validation Accuracy: 49.71590805053711
Iteration: 34. Train Loss: 0.6936367154121399. Validation Accuracy: 51.70454406738281
Iteration: 35. Train Loss: 0.6929294466972351. Validation Accuracy: 49.005680084228516
Iteration: 36. Train Loss: 0.6931363940238953. Validation Accuracy: 49.71590805053711
Iteration: 37. Train Loss: 0.6928470134735107. Validation Accuracy: 50.852272033691406
Iteration: 38. Train Loss: 0.6933615207672119. Validation Accuracy: 51.9886360168457
Iteration: 39. Train Loss: 0.6928941011428833. Validation Accuracy: 52.414772033691406
Iteration: 40. Train Loss: 0.6925554871559143. Validation Accuracy: 52.556819915771484
Iteration: 41. Train Loss: 0.6930360794067383. Validation Accuracy: 50.0
Iteration: 42. Train Loss: 0.6930152773857117. Validation Accuracy: 52.6988639831543
Iteration: 43. Train Loss: 0.6929125785827637. Validation Accuracy: 48.8636360168457
Iteration: 44. Train Loss: 0.6933857202529907. Validation Accuracy: 54.119319915771484
Iteration: 45. Train Loss: 0.692766547203064. Validation Accuracy: 50.28409194946289
Iteration: 46. Train Loss: 0.6931499242782593. Validation Accuracy: 50.568180084228516
Iteration: 47. Train Loss: 0.6927542090415955. Validation Accuracy: 47.727272033691406
Iteration: 48. Train Loss: 0.6929062604904175. Validation Accuracy: 50.0
Iteration: 49. Train Loss: 0.6933912634849548. Validation Accuracy: 47.869319915771484
Iteration: 50. Train Loss: 0.6931474208831787. Validation Accuracy: 51.84659194946289
Iteration: 51. Train Loss: 0.693577229976654. Validation Accuracy: 52.130680084228516
Iteration: 52. Train Loss: 0.6935332417488098. Validation Accuracy: 50.994319915771484
Iteration: 53. Train Loss: 0.6932521462440491. Validation Accuracy: 49.431819915771484
Iteration: 54. Train Loss: 0.6933285593986511. Validation Accuracy: 52.98295593261719
Iteration: 55. Train Loss: 0.6929435729980469. Validation Accuracy: 48.0113639831543
Iteration: 56. Train Loss: 0.6930191516876221. Validation Accuracy: 51.9886360168457
Iteration: 57. Train Loss: 0.6929730772972107. Validation Accuracy: 49.71590805053711
Iteration: 58. Train Loss: 0.6930842995643616. Validation Accuracy: 49.147727966308594
Iteration: 59. Train Loss: 0.6928796768188477. Validation Accuracy: 51.5625
Iteration: 60. Train Loss: 0.6931721568107605. Validation Accuracy: 52.130680084228516
Iteration: 61. Train Loss: 0.6938827037811279. Validation Accuracy: 51.70454406738281
Iteration: 62. Train Loss: 0.6931135058403015. Validation Accuracy: 50.852272033691406
Iteration: 63. Train Loss: 0.6930864453315735. Validation Accuracy: 48.8636360168457
Iteration: 64. Train Loss: 0.6933870315551758. Validation Accuracy: 51.42045593261719
Iteration: 65. Train Loss: 0.6932640075683594. Validation Accuracy: 53.693180084228516
Iteration: 66. Train Loss: 0.6935676336288452. Validation Accuracy: 53.693180084228516
Iteration: 67. Train Loss: 0.6933815479278564. Validation Accuracy: 51.42045593261719
Iteration: 68. Train Loss: 0.6933872103691101. Validation Accuracy: 49.289772033691406
Iteration: 69. Train Loss: 0.692539632320404. Validation Accuracy: 49.289772033691406
Iteration: 70. Train Loss: 0.6932917237281799. Validation Accuracy: 49.289772033691406
Iteration: 71. Train Loss: 0.693217933177948. Validation Accuracy: 52.556819915771484
Iteration: 72. Train Loss: 0.6932273507118225. Validation Accuracy: 51.5625
Iteration: 73. Train Loss: 0.6936159729957581. Validation Accuracy: 49.431819915771484
Iteration: 74. Train Loss: 0.6927348971366882. Validation Accuracy: 50.28409194946289
Iteration: 75. Train Loss: 0.6932668089866638. Validation Accuracy: 48.15340805053711
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Iteration: 4820. Train Loss: 0.61263108253479. Validation Accuracy: 56.818180084228516
Iteration: 4821. Train Loss: 0.6081911325454712. Validation Accuracy: 56.960227966308594
Iteration: 4822. Train Loss: 0.6217280030250549. Validation Accuracy: 61.3636360168457
Iteration: 4823. Train Loss: 0.6339536905288696. Validation Accuracy: 55.255680084228516
Iteration: 4824. Train Loss: 0.6138918399810791. Validation Accuracy: 61.3636360168457
Iteration: 4825. Train Loss: 0.6284343600273132. Validation Accuracy: 56.25
Iteration: 4826. Train Loss: 0.6115025877952576. Validation Accuracy: 55.1136360168457
Iteration: 4827. Train Loss: 0.6062139868736267. Validation Accuracy: 64.48863983154297
Iteration: 4828. Train Loss: 0.6146493554115295. Validation Accuracy: 63.352272033691406
Iteration: 4829. Train Loss: 0.6162334084510803. Validation Accuracy: 58.380680084228516
Iteration: 4830. Train Loss: 0.622169554233551. Validation Accuracy: 60.369319915771484
Iteration: 4831. Train Loss: 0.6154686212539673. Validation Accuracy: 65.19886016845703
Iteration: 4832. Train Loss: 0.6114267706871033. Validation Accuracy: 60.085227966308594
Iteration: 4833. Train Loss: 0.6049310564994812. Validation Accuracy: 56.10795593261719
Iteration: 4834. Train Loss: 0.6020799279212952. Validation Accuracy: 54.97159194946289
Iteration: 4835. Train Loss: 0.6313835382461548. Validation Accuracy: 54.40340805053711
Iteration: 4836. Train Loss: 0.6033760905265808. Validation Accuracy: 57.244319915771484
Iteration: 4837. Train Loss: 0.6175671219825745. Validation Accuracy: 61.647727966308594
Iteration: 4838. Train Loss: 0.6198468208312988. Validation Accuracy: 57.244319915771484
Iteration: 4839. Train Loss: 0.6134922504425049. Validation Accuracy: 58.806819915771484
Iteration: 4840. Train Loss: 0.6315159201622009. Validation Accuracy: 59.51704406738281
Iteration: 4841. Train Loss: 0.6101300716400146. Validation Accuracy: 61.931819915771484
Iteration: 4842. Train Loss: 0.6108498573303223. Validation Accuracy: 60.5113639831543
Iteration: 4843. Train Loss: 0.6070161461830139. Validation Accuracy: 56.6761360168457
Iteration: 4844. Train Loss: 0.6213268637657166. Validation Accuracy: 56.10795593261719
Iteration: 4845. Train Loss: 0.6237767338752747. Validation Accuracy: 62.0738639831543
Iteration: 4846. Train Loss: 0.61817467212677. Validation Accuracy: 61.931819915771484
Iteration: 4847. Train Loss: 0.6159660220146179. Validation Accuracy: 59.943180084228516
Iteration: 4848. Train Loss: 0.6148684024810791. Validation Accuracy: 58.9488639831543
Iteration: 4849. Train Loss: 0.6097829937934875. Validation Accuracy: 56.960227966308594
Iteration: 4850. Train Loss: 0.6132254600524902. Validation Accuracy: 58.380680084228516
Iteration: 4851. Train Loss: 0.6118391156196594. Validation Accuracy: 60.79545593261719
Iteration: 4852. Train Loss: 0.5979594588279724. Validation Accuracy: 62.78409194946289
Iteration: 4853. Train Loss: 0.6226724982261658. Validation Accuracy: 59.51704406738281
Iteration: 4854. Train Loss: 0.6309036016464233. Validation Accuracy: 60.65340805053711
Iteration: 4855. Train Loss: 0.6203624606132507. Validation Accuracy: 62.78409194946289
Iteration: 4856. Train Loss: 0.6330065131187439. Validation Accuracy: 60.227272033691406
Iteration: 4857. Train Loss: 0.6133432984352112. Validation Accuracy: 56.39204406738281
Iteration: 4858. Train Loss: 0.6197800040245056. Validation Accuracy: 53.5511360168457
Iteration: 4859. Train Loss: 0.6184743642807007. Validation Accuracy: 58.806819915771484
Iteration: 4860. Train Loss: 0.621855616569519. Validation Accuracy: 56.960227966308594
Iteration: 4861. Train Loss: 0.6262567043304443. Validation Accuracy: 58.09659194946289
Iteration: 4862. Train Loss: 0.6202670335769653. Validation Accuracy: 56.53409194946289
Iteration: 4863. Train Loss: 0.6261033415794373. Validation Accuracy: 56.25
Iteration: 4864. Train Loss: 0.6133803725242615. Validation Accuracy: 61.07954406738281
Iteration: 4865. Train Loss: 0.6308969855308533. Validation Accuracy: 60.65340805053711
Iteration: 4866. Train Loss: 0.6023369431495667. Validation Accuracy: 57.8125
Iteration: 4867. Train Loss: 0.601612389087677. Validation Accuracy: 62.9261360168457
Iteration: 4868. Train Loss: 0.6060027480125427. Validation Accuracy: 62.9261360168457
Iteration: 4869. Train Loss: 0.6012017726898193. Validation Accuracy: 62.0738639831543
Iteration: 4870. Train Loss: 0.6339138746261597. Validation Accuracy: 59.51704406738281
Iteration: 4871. Train Loss: 0.6294912099838257. Validation Accuracy: 60.79545593261719
Iteration: 4872. Train Loss: 0.6031391620635986. Validation Accuracy: 59.65909194946289
Iteration: 4873. Train Loss: 0.6077345013618469. Validation Accuracy: 58.09659194946289
Iteration: 4874. Train Loss: 0.6143734455108643. Validation Accuracy: 58.522727966308594
Iteration: 4875. Train Loss: 0.6170752644538879. Validation Accuracy: 60.5113639831543
Iteration: 4876. Train Loss: 0.6130874156951904. Validation Accuracy: 58.380680084228516
Iteration: 4877. Train Loss: 0.6143007874488831. Validation Accuracy: 60.5113639831543
Iteration: 4878. Train Loss: 0.6106649041175842. Validation Accuracy: 54.119319915771484
Iteration: 4879. Train Loss: 0.6152575612068176. Validation Accuracy: 57.3863639831543
Iteration: 4880. Train Loss: 0.627366304397583. Validation Accuracy: 58.522727966308594
Iteration: 4881. Train Loss: 0.6126371622085571. Validation Accuracy: 60.085227966308594
Iteration: 4882. Train Loss: 0.6188340783119202. Validation Accuracy: 61.647727966308594
Iteration: 4883. Train Loss: 0.6206964254379272. Validation Accuracy: 55.8238639831543
Iteration: 4884. Train Loss: 0.6047289371490479. Validation Accuracy: 58.380680084228516
Iteration: 4885. Train Loss: 0.6288716793060303. Validation Accuracy: 60.65340805053711
Iteration: 4886. Train Loss: 0.6144806742668152. Validation Accuracy: 62.21590805053711
Iteration: 4887. Train Loss: 0.6151819825172424. Validation Accuracy: 60.9375
Iteration: 4888. Train Loss: 0.6021069288253784. Validation Accuracy: 59.23295593261719
Iteration: 4889. Train Loss: 0.6048505902290344. Validation Accuracy: 58.664772033691406
Iteration: 4890. Train Loss: 0.6126881837844849. Validation Accuracy: 55.681819915771484
Iteration: 4891. Train Loss: 0.6196947693824768. Validation Accuracy: 59.65909194946289
Iteration: 4892. Train Loss: 0.6248962879180908. Validation Accuracy: 61.22159194946289
Iteration: 4893. Train Loss: 0.606656014919281. Validation Accuracy: 57.8125
Iteration: 4894. Train Loss: 0.626093327999115. Validation Accuracy: 56.10795593261719
Iteration: 4895. Train Loss: 0.6130884885787964. Validation Accuracy: 61.505680084228516
Iteration: 4896. Train Loss: 0.6131444573402405. Validation Accuracy: 60.65340805053711
Iteration: 4897. Train Loss: 0.6186776161193848. Validation Accuracy: 58.2386360168457
Iteration: 4898. Train Loss: 0.6176767945289612. Validation Accuracy: 60.5113639831543
Iteration: 4899. Train Loss: 0.6131995916366577. Validation Accuracy: 57.102272033691406
Iteration: 4900. Train Loss: 0.6184263229370117. Validation Accuracy: 58.664772033691406
Iteration: 4901. Train Loss: 0.6235778331756592. Validation Accuracy: 63.6363639831543
Iteration: 4902. Train Loss: 0.6142300367355347. Validation Accuracy: 55.397727966308594
Iteration: 4903. Train Loss: 0.6126133799552917. Validation Accuracy: 61.647727966308594
Iteration: 4904. Train Loss: 0.6076840162277222. Validation Accuracy: 62.0738639831543
Iteration: 4905. Train Loss: 0.6149640083312988. Validation Accuracy: 54.97159194946289
Iteration: 4906. Train Loss: 0.5904919505119324. Validation Accuracy: 60.5113639831543
Iteration: 4907. Train Loss: 0.6221247315406799. Validation Accuracy: 59.375
Iteration: 4908. Train Loss: 0.6333106160163879. Validation Accuracy: 56.25
Iteration: 4909. Train Loss: 0.6152514815330505. Validation Accuracy: 57.8125
Iteration: 4910. Train Loss: 0.6153315305709839. Validation Accuracy: 62.35795593261719
Iteration: 4911. Train Loss: 0.6193504929542542. Validation Accuracy: 59.65909194946289
Iteration: 4912. Train Loss: 0.6051743626594543. Validation Accuracy: 55.1136360168457
Iteration: 4913. Train Loss: 0.617179811000824. Validation Accuracy: 54.97159194946289
Iteration: 4914. Train Loss: 0.6270669102668762. Validation Accuracy: 59.65909194946289
Iteration: 4915. Train Loss: 0.6227912902832031. Validation Accuracy: 59.23295593261719
Iteration: 4916. Train Loss: 0.6126046776771545. Validation Accuracy: 59.09090805053711
Iteration: 4917. Train Loss: 0.6200241446495056. Validation Accuracy: 61.07954406738281
Iteration: 4918. Train Loss: 0.6151767373085022. Validation Accuracy: 57.67045593261719
Iteration: 4919. Train Loss: 0.6175907254219055. Validation Accuracy: 56.53409194946289
Iteration: 4920. Train Loss: 0.6182342767715454. Validation Accuracy: 56.6761360168457
Iteration: 4921. Train Loss: 0.6232109665870667. Validation Accuracy: 57.102272033691406
Iteration: 4922. Train Loss: 0.6166565418243408. Validation Accuracy: 58.380680084228516
Iteration: 4923. Train Loss: 0.6235827803611755. Validation Accuracy: 63.92045593261719
Iteration: 4924. Train Loss: 0.6197315454483032. Validation Accuracy: 55.96590805053711
Iteration: 4925. Train Loss: 0.6326563358306885. Validation Accuracy: 57.95454406738281
Iteration: 4926. Train Loss: 0.6043844819068909. Validation Accuracy: 57.95454406738281
Iteration: 4927. Train Loss: 0.6245386004447937. Validation Accuracy: 55.681819915771484
Iteration: 4928. Train Loss: 0.6246861219406128. Validation Accuracy: 60.9375
Iteration: 4929. Train Loss: 0.6148780584335327. Validation Accuracy: 62.5
Iteration: 4930. Train Loss: 0.6155453324317932. Validation Accuracy: 58.2386360168457
Iteration: 4931. Train Loss: 0.6100617051124573. Validation Accuracy: 56.10795593261719
Iteration: 4932. Train Loss: 0.6210059523582458. Validation Accuracy: 58.2386360168457
Iteration: 4933. Train Loss: 0.6168774366378784. Validation Accuracy: 60.227272033691406
Iteration: 4934. Train Loss: 0.6025316715240479. Validation Accuracy: 55.539772033691406
Iteration: 4935. Train Loss: 0.62120521068573. Validation Accuracy: 65.48295593261719
Iteration: 4936. Train Loss: 0.6164575219154358. Validation Accuracy: 57.8125
Iteration: 4937. Train Loss: 0.6108257174491882. Validation Accuracy: 59.51704406738281
Iteration: 4938. Train Loss: 0.6080488562583923. Validation Accuracy: 59.943180084228516
Iteration: 4939. Train Loss: 0.6147021055221558. Validation Accuracy: 67.32954406738281
Iteration: 4940. Train Loss: 0.6442474722862244. Validation Accuracy: 58.09659194946289
Iteration: 4941. Train Loss: 0.6132006049156189. Validation Accuracy: 57.244319915771484
Iteration: 4942. Train Loss: 0.6240121722221375. Validation Accuracy: 58.380680084228516
Iteration: 4943. Train Loss: 0.5974135994911194. Validation Accuracy: 57.3863639831543
Iteration: 4944. Train Loss: 0.6215579509735107. Validation Accuracy: 60.5113639831543
Iteration: 4945. Train Loss: 0.6176208257675171. Validation Accuracy: 60.227272033691406
Iteration: 4946. Train Loss: 0.6267193555831909. Validation Accuracy: 61.3636360168457
Iteration: 4947. Train Loss: 0.6219105124473572. Validation Accuracy: 60.227272033691406
Iteration: 4948. Train Loss: 0.6251431107521057. Validation Accuracy: 61.07954406738281
Iteration: 4949. Train Loss: 0.6216304898262024. Validation Accuracy: 61.931819915771484
Iteration: 4950. Train Loss: 0.6268297433853149. Validation Accuracy: 58.9488639831543
Iteration: 4951. Train Loss: 0.615439236164093. Validation Accuracy: 58.09659194946289
Iteration: 4952. Train Loss: 0.6204626560211182. Validation Accuracy: 55.681819915771484
Iteration: 4953. Train Loss: 0.6189408898353577. Validation Accuracy: 56.818180084228516
Iteration: 4954. Train Loss: 0.611869752407074. Validation Accuracy: 62.21590805053711
Iteration: 4955. Train Loss: 0.617424726486206. Validation Accuracy: 55.539772033691406
Iteration: 4956. Train Loss: 0.6243166923522949. Validation Accuracy: 58.2386360168457
Iteration: 4957. Train Loss: 0.6040756106376648. Validation Accuracy: 63.210227966308594
Iteration: 4958. Train Loss: 0.6163157820701599. Validation Accuracy: 53.5511360168457
Iteration: 4959. Train Loss: 0.6004520058631897. Validation Accuracy: 58.664772033691406
Iteration: 4960. Train Loss: 0.619194746017456. Validation Accuracy: 61.07954406738281
Iteration: 4961. Train Loss: 0.6025323867797852. Validation Accuracy: 64.20454406738281
Iteration: 4962. Train Loss: 0.6118331551551819. Validation Accuracy: 55.1136360168457
Iteration: 4963. Train Loss: 0.605509340763092. Validation Accuracy: 54.40340805053711
Iteration: 4964. Train Loss: 0.6311138272285461. Validation Accuracy: 62.64204406738281
Iteration: 4965. Train Loss: 0.6167426705360413. Validation Accuracy: 62.78409194946289
Iteration: 4966. Train Loss: 0.6216997504234314. Validation Accuracy: 56.960227966308594
Iteration: 4967. Train Loss: 0.6156224608421326. Validation Accuracy: 58.522727966308594
Iteration: 4968. Train Loss: 0.6074571013450623. Validation Accuracy: 57.52840805053711
Iteration: 4969. Train Loss: 0.6205114722251892. Validation Accuracy: 57.102272033691406
Iteration: 4970. Train Loss: 0.6255614161491394. Validation Accuracy: 60.65340805053711
Iteration: 4971. Train Loss: 0.6116440296173096. Validation Accuracy: 59.51704406738281
Iteration: 4972. Train Loss: 0.6197398900985718. Validation Accuracy: 58.664772033691406
Iteration: 4973. Train Loss: 0.5992456078529358. Validation Accuracy: 58.522727966308594
Iteration: 4974. Train Loss: 0.6248807907104492. Validation Accuracy: 57.52840805053711
Iteration: 4975. Train Loss: 0.6098843216896057. Validation Accuracy: 61.22159194946289
Iteration: 4976. Train Loss: 0.6122855544090271. Validation Accuracy: 55.8238639831543
Iteration: 4977. Train Loss: 0.6047680377960205. Validation Accuracy: 60.369319915771484
Iteration: 4978. Train Loss: 0.6184876561164856. Validation Accuracy: 60.369319915771484
Iteration: 4979. Train Loss: 0.6008101105690002. Validation Accuracy: 60.085227966308594
Iteration: 4980. Train Loss: 0.6056514978408813. Validation Accuracy: 56.25
Iteration: 4981. Train Loss: 0.618472158908844. Validation Accuracy: 55.96590805053711
Iteration: 4982. Train Loss: 0.641120433807373. Validation Accuracy: 55.539772033691406
Iteration: 4983. Train Loss: 0.6098236441612244. Validation Accuracy: 59.375
Iteration: 4984. Train Loss: 0.6144724488258362. Validation Accuracy: 56.39204406738281
Iteration: 4985. Train Loss: 0.6247982978820801. Validation Accuracy: 58.2386360168457
Iteration: 4986. Train Loss: 0.6112855076789856. Validation Accuracy: 56.10795593261719
Iteration: 4987. Train Loss: 0.6147253513336182. Validation Accuracy: 60.79545593261719
Iteration: 4988. Train Loss: 0.6162710785865784. Validation Accuracy: 57.102272033691406
Iteration: 4989. Train Loss: 0.6229048371315002. Validation Accuracy: 57.3863639831543
Iteration: 4990. Train Loss: 0.6098870635032654. Validation Accuracy: 56.10795593261719
Iteration: 4991. Train Loss: 0.6189953684806824. Validation Accuracy: 58.09659194946289
Iteration: 4992. Train Loss: 0.6142786741256714. Validation Accuracy: 56.6761360168457
Iteration: 4993. Train Loss: 0.6163108348846436. Validation Accuracy: 59.09090805053711
Iteration: 4994. Train Loss: 0.6291056275367737. Validation Accuracy: 63.210227966308594
Iteration: 4995. Train Loss: 0.6215679049491882. Validation Accuracy: 57.67045593261719
Iteration: 4996. Train Loss: 0.6046907305717468. Validation Accuracy: 60.227272033691406
Iteration: 4997. Train Loss: 0.6106722354888916. Validation Accuracy: 62.21590805053711
Iteration: 4998. Train Loss: 0.631191074848175. Validation Accuracy: 60.9375
Iteration: 4999. Train Loss: 0.630180299282074. Validation Accuracy: 54.97159194946289
Finished Training
Learning curves¶
In [49]:
# Plot the cost
plt.plot(train_costs)
plt.plot(val_costs)
plt.ylabel('Cost')
plt.xlabel('iterations')
plt.title('Learning curves')
plt.legend(['Train loss', 'Test loss'])
plt.show()
Accuracy vs iterations¶
In [50]:
# Plot the val accuracy
plt.plot(train_acc)
plt.plot(val_acc)
plt.ylabel('Accuracy')
plt.xlabel('iterations')
plt.title('Accuracy vs Iteration plot')
plt.legend(['Train acc', 'Test acc'])
plt.show()
Accuracy vs L¶
In [51]:
acc = []
for L in range(1, 21):
inputs, labels = get_batch(100, L)
outputs = model(inputs)
_, predicted = torch.max(outputs.view(-1, 2).data, 1)
acc.append(accuracy_score(labels.flatten().numpy(), predicted.numpy()))
In [52]:
# Plot the val accuracy
plt.plot(range(1, 21), acc)
plt.ylabel('Accuracy')
plt.xlabel('Sequence length - L')
plt.title('Accuracy vs L plot')
plt.show()
The trend remains the same even when trained on larger values of L. This is because the network has to model long-range dependencies as L increases